Background: Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics. 
Methods: We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family. Models are clustered using a custom similarity metric, and a visual interface is provided that allows a researcher to interactively compare the structures of pairs of models as well as view simulation results. 
Results: We illustrate the usefulness of MOSBIE via two case studies in the cell signaling domain. We also present feedback provided by domain experts and discuss the benefis, as well as the limitations, of the approach. 
Keywords: visualization; visual computing; rule-based modeling; cell signaling

The TLR4 family of models. A similarity comparison between models TLR4 v15 and TLR4 RPS v1 in the TLR4 family.

MOSBIE: A Tool for Comparison and Analysis of Rule-Based Biochemical Models

Background: Molecular and systems biologists are tasked with the comprehension and analysis of incredibly complex networks of biochemical interactions, called pathways, that occur within a cell. Through interviews with domain experts, we identified four common tasks that require an understanding of the causality within pathways, that is, the downstream and upstream relationships
between proteins and biochemical reactions, including: visualizing downstream consequences of perturbing a protein; finding the shortest path between two proteins; detecting feedback loops within the pathway; and identifying common downstream elements from two or more proteins. 
Results: We introduce ReactionFlow, a visual analytics application for pathway analysis that emphasizes the structural and causal relationships amongst proteins, complexes, and biochemical reactions within a given pathway. To support the identified causality analysis tasks, user interactions allow an analyst to filter, cluster, and select pathway components across linked views. Animation is used to highlight the flow of activity through a pathway. 
Conclusions: We evaluated ReactionFlow by providing our application to two domain experts who have significant experience with biomolecular pathways, after which we conducted a series of in-depth interviews focused on each of the four causality analysis tasks. Their feedback leads us to believe that our techniques could be useful to researchers who must be able to understand and analyze the complex nature of biological pathways. 
ReactionFlow is available at <a href="https://github.com/CreativeCodingLab/ReactionFlow">https://github.com/CreativeCodingLab/ReactionFlow</a>. 
Keywords: Pathway visualization; Biological networks; Causality analysis; Topological ordering

Downstream consequences if a protein/complex is &dlquo;knocking out&drquo; in the defects in vitamin and cofactor metabolism pathway: (a) GIF protein (b) TCll:Cbl complex (c) MTRR:MTR complex.

ReactionFlow: An Interactive Visualization Tool for Causality Analysis in Biological Pathways

Background: Molecular activation pathways are inherently complex, and understanding relations across many biochemical reactions and reaction types is difficult. Visualizing and analyzing a pathway is a challenge due to the network size and the diversity of relations between proteins and molecules. 
Results: In this paper, we introduce PathwayMatrix, a visualization tool that presents the binary relations between proteins in the pathway via the use of an interactive adjacency matrix. We provide filtering, lensing, clustering, and brushing and linking capabilities in order to present relevant details about proteins within a pathway. 
Conclusions: We evaluated PathwayMatrix by conducting a series of in-depth interviews with domain experts who provided positive feedback, leading us to believe that our visualization technique could be helpful for the larger community of researchers utilizing pathway visualizations. 
PathwayMatrix is freely available at <a href="https://github.com/CreativeCodingLab/PathwayMatrix">https://github.com/CreativeCodingLab/PathwayMatrix</a>. 
Keywords: Pathway visualization; Biological networks; Matrix visualization; Binary Relationships

Visualizing protein binary relationships of the HIV life cycle by different tools: BioFabric (top panel), Cytoscape (middle panel), Hive Plot (bottom left panel), and PathwayMatrix (bottom right panel).

PathwayMatrix: Visualizing Binary Relationships between Proteins in Biological Pathways

PhD candidate Paul Murray presents his research in biological data visualization techniques. 
The Pathway Commons web service brings together several large biological databases, with tens of thousands of biological entities and interactions that can be traversed and queried through the Pathway Commons API. Query results are returned using the BioPax format (which is an RDF/XML OWL file), and each BioPax file contains a number of different relationship types. Rather than try to encompass a wide variety of node types and link types into a single visualization, a linked-view visualization approach is employed that presents each type of relationship in its own view, with linked interactions between views. This system is being integrated with the Pathway Commons API to enable effective visual queries of complex biological data.

Screenshot of linked-view visualization approach.

Exploring BioPax and the Pathway Commons API Using Linked Views

Dynamic network visualization has been a challenging topic for dynamic networks analysis, especially for spatially embedded networks like brain networks.  In this paper, we present an animated interactive visualization design that combines enhanced node-link diagrams and distance matrix layouts to assist neuroscientists in their exploration of dynamic brain networks and that enables them to understand how functional connections relate to the spatial structure of the brain.  Our visualization also provides the ability to observe the evolution of a network, the change in the community identities, and node behavior over time.

Screenshot of our visualization tool showing the coordinated dual-representation of dynamic network data.

Visualizing Dynamic Brain Networks Using an Animated Dual-Representation

Collaborative visual analytics that feature mixtures of spatial and nonspatial data occur across disciplines, and are particularly common in bioinformatics, neuroscience and geospatial analysis. In this work we analyze, from a human-centric perspective, data collected from the design and evaluation of three successful visual analysis tools, spanning seven case studies. We focus on the importance of the users&rsquo; background to the design process, and we discuss the importance of visual scaffolding to such collaborative, integrated spatial and nonspatial visual analysis tools. Scaffolding is a psychology concept which denotes the support given during a learning process. We further present evidence that spatial and nonspatial coordinated views can serve as a form of visual scaffolding for expert-level, collaborative visual analyses. 
Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Visual analysis processes
and workflows - Cognitive and perceptual aspects of visual analytics - Collaborative visual analytics

Two visual analyses in a visual framework for the integration, comparison, and exploration of correlations in spatial and nonspatial geriatric data.

Visual Scaffolding in Integrated Spatial and Nonspatial Analysis

EVL PhD candidate Chihua Ma presents approaches for visualizing biological data at EVL's summer tech talks. 
As biological systems dynamically change over time in nature, the measurement of timing effects should be taken into consideration to better understand the evolution of biological processes. Currently, two main visualization techniques for representing time are time-to-time mapping (animation) and time-to-space mapping (timeline plots and space-time cube). In addition, biological systems include both spatial and non-spatial information. Using multiple and coordinated views is considered to be a good approach to integrate spatial and non-spatial features. 
This presentation introduces relevant approaches for visualizing spatial/non-spatial time-based data followed by two case studies: visualization of dynamic mouse brain networks and visualization of probability distributions at the steady states in the FFL network motif.

Space Attribute Cube Visualization

Visualization of time-series biological data with spatial and non-spatial features embedded

When investigating multidimensional data sets with very large numbers of objects and/or a very large number of dimensions, a variety of visualization methods can be employed in order to represent the data effectively and to enable the user to explore the data at different levels of detail. A common strategy for encoding multidimensional data for visual analysis is to use dimensionality reduction techniques that project data from higher dimensions onto a lower-dimensional space. In this paper, we focus on projection techniques that output 2D or 3D scatterplots which can then be used for a range of data analysis tasks. Existing taxonomies for multidimensional data projections focus primarily on tasks in order to evaluate the human perception of class or cluster separation and/or preservation. However, real-world data analysis of complex data sets often includes other tasks besides cluster separation, such as: cluster identification, similarity seeking, cluster ranking, comparisons, counting objects, etc. A contribution of this paper is the identification of subtasks grouped into four main categories of data analysis tasks. We believe that this user-centric task categorization can be used to guide the organization of multidimensional data projection layouts. Moreover, this taxonomy can be used as a guideline for visualization designers when faced with complex data sets requiring dimensionality reduction. Our taxonomy aims to help designers evaluate the effectiveness of a visualization system by providing an expanded range of relevant tasks. These tasks are gathered from an extensive study of visual analytics projects across real-world application domains, all of which involve multidimensional projection. In addition to our survey of tasks and the creation of the task taxonomy, we also explore in more detail specific examples of how to represent data sets effectively for particular tasks. These case studies, while not exhaustive, provide a framework for how specifically to reason about tasks and to decide on visualization methods. That is, we believe that this taxonomy will help visualization designers to determine which visualization methods are appropriate for specific multidimensional data projection tasks. 
Keywords: Multidimensional data analysis, task taxonomy, multidimensional data projection, user-centric evaluation.

Non-convex hulls computed from enclosing surfaces isodistant to cluster using LSP projection.

A user-centric taxonomy for multidimensional data projection tasks

Background: Biologists make use of pathway visualization tools for a range of tasks, including investigating inter-pathway connectivity and retrieving details about biological entities and interactions. Some of these tasks require an understanding of the hierarchical nature of elements within the pathway or the ability to make comparisons between multiple pathways. We introduce a technique inspired by LineSets that enables biologists to fulfill these tasks more effectively. 
Results: We introduce a novel technique, Extended LineSets, to facilitate new explorations of biological pathways. Our technique incorporates intuitive graphical representations of different levels of information and includes a well designed set of user interactions for selecting, filtering, and organizing biological pathway data gathered from multiple databases. 
Conclusions: Based on interviews with domain experts and an analysis of two use cases, we show that our technique provides functionality not currently enabled by current techniques, and moreover that it helps biologists to better understand both inter-pathway connectivity and the hierarchical structure of biological elements within the pathways.

Comparison of the same dataset realized with (a) LineSets, showing set information and network information simultaneously leads to visual clutter; (b) Extended LineSets, mitigates visual clutter via a more effective depiction of category information.

Extended LineSets: A visualization technique for the interactive inspection of biological pathways

This paper describes an artwork created in response to a question about the role of the artist in communicating climate change issues. The artwork, titled Turbulent World, incorporates turbulence and surprise as a means to visualize the potential instability of our culture and the environment due to climatic changes indicated by increased worldwide temperatures. The artwork makes use of a custom fluid engine that can represent any amount of turbulence and energy. A dataset encoding a simulation of rising surface-air temperatures over the next century is mapped to the turbulence system; and the visualization is updated as the months and years flow by, based on the projected temperatures at different areas of the world. That is, the increased turbulence of the system causes a representation of a map of the world to become distorted in different ways. A secondary view is overlaid, showing numerical data and providing a more dispassionate display of the inexorable increase in world temperature. 
Keywords: climate change, art-science, software art

Screen capture from the artwork as it shows the average surface-air temperature across the world for September 2067, using the projected values generated by the CM2.1 model developed by the Geophysical Fluid Dynamics Laboratory.

TurbulentWorld: An Artwork Indicating the Impact of Climate Change

Thanks to advances in non-invasive technologies such as functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI), highly-detailed maps of brain structure and function can now be collected. In this context, brain connectomics have emerged as a fast growing field that aims at understanding these comprehensive maps of brain connectivity using sophisticated computational models. In this paper we present BRAINtrinsic, an innovative web-based 3D visual analytics tool that allows users to intuitively and iteratively interact with connectome data. Moreover, BRAINtrinsic implements a novel visualization platform that reconstructs connectomes&rsquo; intrinsic geometry, i.e., the topological space as informed by brain connectivity, via dimensionality reduction. BRAINtrinsic is implemented with virtual reality in mind and is fully compatible with the Oculus Rift technology. Last, we demonstrate its effectiveness through a series of case studies involving both structural and resting-state MR imaging data. 
Keywords: connectomics, connectome datasets, intrinsic geometry, neuroimaging

Intrinsic geometry of the tractography-derived structural and the resting-state fMRI connectome, as well as the locations of rich-club regions in these spaces.

BRAINtrinsic: A Virtual Reality-Compatible Tool for Exploring Intrinsic Topologies of the Human Brain Connectome

Senior data analyst, Wei Xie in the Alibaba Safety and Security Intelligence Center will present Alibaba's work on large-scale data visualization and analytics aimed at identifying and analyzing risks and frauds in customer transaction data records. The presentation will provide and introduction to The Alibaba Group, and overview of the cyber-crime challenge currently being faced in China, and a discussion of big data vs. cyber-crime with prospective approaches and solutions. 
Speaker: Wei Xie 
Wei Xie, director of Alibaba's Business Safety Agency, is the leader of SSIC (Safety and Security Intelligence center). SSIC of Alibaba group is China’s first data visualization center based on real-time operation data in the area of online safety and security. Mr. Xie has over 15 years of experiences in the field of IT as well as safety and security. He is also the expert on anti-fraud, data safety. Conducting university collaborations is one of his main responsibilities as well.

When Big Data Meets Cyber-Crime

EVL PhD candidate, Jillian Aurisano present her research in data visualization and exploration using a conversational interface. 
Visual data exploration poses challenges for &rsquo;InfoVis Novices&rsquo;. A &rsquo;conversational interface&rsquo; which would enable users to generate and interact with visualizations through natural language and gestures, while maintaining a history of the data exploration, has the potential to ameliorate many of these challenges. We performed an exploratory, observational study designed to examine the role of such a conversational interface in visual data exploration. We simulated a conversational interface, using a remote human mediator, with multiple cycles of visualization construction. We believe analysis of this data will yield concrete design goals for conversational interfaces in information visualization. Research results and future work will be presented.

&ldquo;Show Me Data.&rdquo; Observational Study of a Conversational Interface in Visual Data Exploration

EVL PhD alumna and director of the University of Arkansas Little Rock&rsquo;s Emerging Analytics Center, Carolina Cruz-Neira hosts the inaugural open house of her latest Virtual Reality Center. 
Among Cruz-Neira&rsquo;s accomplishments is as the co-creator of the CAVE&trade; Virtual Realtiy Environment, developed as part of her PhD research at EVL. She is also recognized by having founded and led very successful virtual reality research centers, like the Virtual Reality Applications Center at Iowa State University, the Louisiana Immersive Technologies Enterprise and now the Emerging Analytics Center (EAC). 
EAC&rsquo;s work is centered on interdisciplinary projects and it covers a wide range of topics from the development and use of large-scale one-of-a-kind systems to every day portable technologies such as smart phones, glasses and tablets as well as pioneering applications for these technologies in many disciplines and markets. 
EAC&rsquo;’s projects are in areas such as large data visualization, data analytics, training environments, software architectures for visualization and virtual reality, technology development, modeling and simulation, museum installations, and tech-transfer to many of our sponsors and collaborators. 
Join Cruz-Neira and EAC faculty, researchers, and students at the facility open house October 16-17, 2015 at the Emerging Analytics Center, UALR EIT - 420. 
Register: <a href="discovervirtualreality.eventbrite.com">discovervirtualreality.eventbrite.com</a>

EAC Open House Flyer

Carolina Cruz-Neira

EVL Alumna Opens Emerging Analytics Center at University of Arkansas

The week of October 25, 2015, the international IEEE Visualization (VIS) 2015 conference is being held in downtown Chicago, and the Electronic Visualization Laboratory (EVL), part of the Computer Science (CS) department at the University of Illinois at Chicago, has a leadership role in helping ensure its success. 
EVL director Maxine Brown serves as General Co-chair with Michael E. Papka of Argonne National Laboratory (who is also a CS/EVL alumnus). EVL CS faculty G. Elisabeta Marai is co-chair of the Doctoral Colloquium. EVL CS faculty Angus Forbes and EVL School of Design faculty Daria Tsoupikova are co-chairs of the Arts Program (VISAP). EVL director of research and CS faculty Andy Johnson serves on the Program Committees of the Scientific Visualization (SciVis) and VISAP tracks. EVL senior research scientist and director emeritus Dan Sandin is participating in VISAP as an invited artist and panelist. 
EVL CS students are also participating. Jillian Aurisano and Tim Luciano are serving as Student Volunteers. Aurisano also has two accepted posters: &ldquo;Show Me Data,&rdquo; which received an honorable mention, is about a conversational interface in visual data exploration that is being done with CS advisor Andy Johnson and professor Barbara Di Eugenio; and, &ldquo;AlignmentVis: Visual Analytics for Ontology Matching,&rdquo; which describes her work from the Data and Web Semantics course taught by professor Isabel Cruz. Angus Forbes&rsquo; student Alessandro Chetta is presenting a paper in the Workshop on Visual Analytics in Health Care. Forbes&rsquo; student Kyle Almryde received honorable mention in the SciVis Data Contest for his web-based 3D visualization application entitled &ldquo;Halos in a Dark Sky: Interactively Exploring the Structure of Dark Matter Halo Merger Trees.&rdquo; 
In addition to the above, many EVL alumni are also involved in organizing this conference, and many more will be attending the one week event. The UIC CS department is one of the sponsors. 
The conference press release below provides additional details about <a href="http://ieeevis.org">IEEE VIS 2015</a>. The <a href="http://visap.uic.edu/2015/">IEEE VIS Arts Program</a> is open to the public for two weeks, from October 19-30, and is being held at the School of the Art Institute of Chicago&rsquo;s (SAIC) LeRoy Neiman Center, 37 S. Wabash. 
* * * * * * * * * * * * * * * * 
2015 IEEE Visualization Conference Connects Latest in Research to Real World Practice 
CHICAGO, 12 October 2015 - Over a thousand data visualization specialists are expected at the 2015 IEEE Visualization (VIS) conference in Chicago this month. For one week starting October 25, VIS will host the largest annual gathering of academics, practitioners, and researchers focused on making every variety of dataset visually understandable. 
After its successful meeting in Paris last year, the international VIS conference is returning to the United States, where organizers hope to attract a record number of young practitioners seeking opportunities to meet and mingle with the senior members and leaders in their field. This year&rsquo;s program includes several returning events aimed at encouraging such interactions, such as the Doctoral Colloquium and Digital Compass activities, along with a new industry-targeted Practitioner Event. 
<ldquo;This conference is the best opportunity all year for the data visualization community to see and share the latest advancements, tools, and techniques,rdquo; said Michael E. Papka, VIS 2015 co-chair. &ldquo;From the academic to the applied, VIS has something to offer everybody working in the field today.&rdquo; 
VIS 2015 papers talks and panels are organized into three tracks: Visual Analytics Science and Technology (VAST), Information Visualization (InfoVis), and Scientific Visualization (SciVis). These main track offerings and a three-day exhibition begin on Tuesday October 27. Three co-located symposiums are held on the first two days of VIS 2015: Visualization in Data Science (VDS), Large Data Analysis and Visualization (LDAV), and Visualization for Cyber Security (VizSec). The VIS offerings on Sunday October 25 and Monday October 26 also include eight workshops and nine tutorials. 
The VIS 2015 Arts Program features a papers track and a two-week public art exhibition at the School of the Art Institute of Chicago&rsquo;s (SAIC’s) LeRoy Neiman Center, which is located across the street from the conference venue, the Palmer House Hilton, in Chicago&rsquo;s downtown Loop district. 
IEEE VIS is sponsored by IEEE’s Computer Society. Additional support comes from academic, industry, and government research organizations. <a href="http://ieeevis.org">Register and learn more about the conference program</a>. Follow us on Twitter, like our Facebook page, or visit our LinkedIn page. 
CONTACT: 
Laura Wolf (Argonne National Laboratory) 
IEEE Visualization 2015 Coordinating Chair 
<a href="mailto:info@ieeevis.org">info@ieeevis.org</a>

&ldquo;Halos in a Dark Sky: Interactively Exploring the Structure of Dark Matter Halo Merger Trees,&rdquo; received honorable mention in the IEEE VIS 2015 SciVis Data Contest.

EVL has leadership roles in international IEEE Visualization 2015 conference

A natural language interface to visual data exploration would allow a user to directly specify questions through speech, allowing the user to focus on higher-order tasks, such as hypothesis generation and question formulation. 
However, visual data exploration involves repeated cycles of visualization construction and interaction, as well as reasoning across many visualizations generated over the course of an exploratory session. A &rsquo;conversational interface&rsquo;, which maintains a dialog with the user through natural language and gestures, could support these complex tasks. 
We conducted an observational, exploratory study to observe the interaction between a subject and a remote data analysis expert (DAE) who assists the subject in an exploratory data analysis task.

Research subject involved in an exploratory data analysis task using the EVL Cyber-Commons tiled display wall.

&ldquo;Show me data&rdquo; Observational study of a conversational interface in visual data exploration

To announce OpenGrid, a new and innovative website enabling the public to navigate City data using an intuitive and visual interface, the City of Chicago&rsquo;s Department of Innovation &amp; Technology wanted a high-tech stage. Several stages, in fact, so that invited media and partners could listen to presentations in one environment and then move to another for demos and hands-on experiences. They came to the Electronic Visualization Laboratory (EVL) at the University of Illinois at Chicago to host the event. 
Brenna Berman, the City&rsquo;s Chief Information Officer (CIO) and Commissioner of the Department of Innovation & Technology, and Tom Schenk, Jr., Chief Data Officer (CDO), presented OpenGrid in EVL&rsquo;s Cyber-Commons room, followed by multiple demos in the CAVE2&trade; Hybrid Reality Environment. They explained OpenGrid&rsquo;s technical capabilities, but also its human capabilities - as it makes City data more readily accessible to Chicago communities, providing its citizens with the information they need to solve problems relevant to their communities. 
Data portals organize data by individual datasets, making it difficult to combine data and see what is happening in one&rsquo;s neighborhood. OpenGrid is a public website that makes the Chicago Data Portal more easily available to all residents. Users can select areas on a map of the City, on which to graphically view locations of inspections, street closures, potholes, crimes, and other activities. 
Chicago&rsquo;s investment in open data, along with strategic partnerships with civic tech volunteers and pro bono corporations, has also helped improve the city&rsquo;s services. EVL is proud to have helped this initiative! 
Learn more about <a href="http://opengrid.io/">OpenGrid</a>. 
See OpenGrid collaborator, University of Chicago&rsquo;s <a href="https://ci.uchicago.edu/press-releases/new-opengrid-app-illuminates-city-data-residents">press release.</a>

Brenna Berman, City of Chicago&rsquo;s Chief Information Officer (CIO) and Commissioner of the Department of Innovation &amp; Technology, introduces OpenGrid on the UIC EVL Cyber-Commons wall.

City of Chicago Partners with UIC/EVL to Announce OpenGrid

Wednesday, March 9, 2016, 1:00 PM 
EVL Cyber-Commons, Room 2068 ERF 
MS candidate and Italian exchange student, Andrea Purgato presents his research into methods for the GPU-based computation to define the structure of a dynamic social or biological network. Emphasis is placed expressly on brain network computation, which is computationally expensive. The goal is to speed up the computation process by using high performance computing techniques, exploiting the computational power and the architecture of GPU. Examples centered on a real-time visualization of the computed networks, using GPU-accelerated computing will be presented. The goal is to provide researchers' greater insight into the structures of dynamic networks.

Network visualization spplication screenshot.

Network Computation with GPU

Study of the behavior of individual members in communities of dynamic networks can help neuroscientists to understand how interactions between neurons in brain networks change over time. Visualization of those temporal features is challenging, especially for networks embedded within spatial structures, such as brain networks. In this article, the authors present the design of SwordPlots, an interactive multi-view visualization system to assist neuroscientists in their exploration of dynamic brain networks from multiple perspectives. Their visualization helps neuroscientists to understand how the functional behavior of the brain changes over time, how such behaviors are related to the spatial structure of the brain, and how communities of neurons with similar functionality evolve over time. To evaluate their application, they asked neuroscientists to use SwordPlots to examine four different mouse brain data sets. Based on feedback, their visualization design can provide neuroscientists with the ability to gain new insights into the properties of dynamic brain networks.

A SwordPlot includes multiple integrated components for representing time-series data and the community membership of a node in a dynamic network.

SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks

Understanding how brain regions are interconnected is an important topic within the domain of neuroimaging. Advances in non-invasive technologies enable larger and more detailed images to be collected more quickly than ever before. These data contribute to create what is usually referred to as a connectome, that is, a comprehensive map of neural connections. The availability of connectome data allows for more interesting questions to be asked and more complex analyses to be conducted. In this paper we present a novel web-based 3D visual analytics tool that allows user to interactively explore the intrinsic geometry of the connectome. That is, brain data that has been transformed through a dimensionality reduction step, such as multidimensional scaling (MDS), Isomap, or t-distributed stochastic neighbor embedding (t-SNE) techniques. We evaluate our tool through a series of real-world case studies, demonstrating its effectiveness in aiding domain experts for a range of neuroimaging tasks.

This figure shows the main view of the web-based 3D visual analytics tool that allows user to interactively explore the intrinsic geometry of the connectome.

Intrinsic Geometry Visualization for the Interactive Analysis of Brain Connectivity Patterns

This paper presents a guideline for visualization designers who want to choose appropriate techniques for enhancing tasks involving multidimensional projection. Specifically, we adopt a user-centric approach in which we take user perception into consideration. Here, we focus on projection techniques that output 2D or 3D scatterplots that can then be used for a range of common data analysis tasks, which we categorize as pattern identification tasks, relation-seeking tasks, membership disambiguation tasks, or behavior comparison tasks. Our user-centric task categorization can be used to effectively guide the organization of multidimensional data projection layouts. Moreover, we present real-world examples that demonstrate effective choices made by visualization designers faced with complex datasets requiring dimensionality reduction. 
Keywords: Multidimensional data analysis, task taxonomy, multidimensional data projection, user-centric evaluation.

Comparison of INFOVIS04 document data set map using Neighbor Joining and LSP projections: Four dierent topics of information visualization are identied by coloring points.

Choosing Visualization Techniques for Multidimensional Data Projection Tasks: A Guideline with Examples

Thesis Presentation 
Wednesday, March 30, 2016, 1:00 PM 
Room 2068 ERF, EVL Cyber-Commons 
EVL MS candidate and Italian exchange student, Davide Tantillo presents his research in human-computer interaction on large vertical displays. 
Large vertical displays are starting to spread around different realities. Exhibitions, fairs, workplaces, and often schools and universities are employing them for their capabilities. They have very wide resolutions capable of hosting a lot of information at the same time. Furthermore, they are connected to internet where - it is easily known - an infinite quantity of information is immediately usable. However, it is not easy to exploit efficiently these resolutions, but disciplines such as Data Visualization are currently studying how to present information on these devices. Another important topic about large vertical displays is how users should interact with them. Mice and keyboards, touch detection systems, or other strategies can be employed. There is no winner among them and their preference depend on the application. However, no precise guidelines are present on how use them and on how provide an interface for large vertical displays with these input systems. Lastly, lack in the literature of large displays is that we do not know how people behave using them. There are some interesting studies, but they are limited and often neglect the existence of wider conclusions, leading to divergent conclusions. 
This research studies the behavior of humans in a collaborative-competitive task using a large vertical display with multi-touch capabilities. More precisely, the proposed task is the scheduling session for the talks in a scientific conference. Given this task and studying users is designed to better understand how people interact with the display and with one another as a group. We will code and analyze characteristics such as the group&rsquo;s verbal communication, visual attention, shape and the users&rsquo; proximity to the display. Eventually, the set of codes will be combined to understand if some are connected towards better understanding user behavior.

D. Tantillo Thesis Prsentation Slide Set

Large Multitouch Vertical Displays in Multi-User Competitive Tasks

PhD, 2019

Timothy Luciani

This paper describes novel methods for constructing the intrinsic geometry of the human brain connectome using dimensionality-reduction techniques. We posit that the high-dimensional, complex geometry that represents this intrinsic topology can be mathematically embedded into lower dimensions using coupling patterns encoded in the corresponding brain connectivity graphs.
We tested both linear and nonlinear dimensionality-reduction techniques using the diffusion-weighted structural connectome data acquired from a sample of healthy subjects. Results supported the nonlinearity of brain connectivity data, as linear reduction techniques such as the multidimensional scaling yielded inferior lower-dimensional embeddings. To further validate our results, we
demonstrated that for tractography-derived structural connectome more influential regions such as rich-club members of the brain are more centrally mapped or embedded. Further, abnormal brain connectivity can be visually understood by inspecting the altered geometry of these three-dimensional (3D) embeddings that represent the topology of the human brain, as illustrated using simulated lesion studies of both targeted and random removal. Last, in order to visualize brain&rsquo;s intrinsic topology we have developed software that is compatible with virtual reality technologies, thus allowing researchers to collaboratively and interactively explore and manipulate brain connectome data. 
Keywords: Diffusion MRI, Tractography, Dimensionality reduction, Virtual reality, Connectomics

The anatomy of a human brain shown in BRAINtrinsic. Inset shows the Oculus Rift technology being used o visualize brain connectome data using the proposed approach.

The intrinsic geometry of the human brain connectome

The use of electronic health records (EHRs) in clinical environments provides new opportunities for nurses to integrate data analyses into their practice. While having access to these records has many benefits, the act of recording, retrieving, and analyzing this data can nonetheless introduce communication issues, as navigating and interpreting large amounts of heterogeneous data can be difficult, and conclusions can be hard to validate. In this paper, we describe a series of integrated visual interfaces to help nurses document and reason about patient data and about clinicians&rsquo; understanding of patient data. The interfaces present the output of a predictive algorithm that makes use of historical EHR data, patient vital signs, and nurse handoff reports in order to classify a patient in terms of their likelihood of experiencing clinical events. Furthermore, the interfaces enable the nurses to quickly explore the original data and to examine other nurses&rsquo; interpretation of patient activity during previous shifts. We present a series of usage scenarios that introduce our interactive visualization tools in the context of real-world healthcare situations. 
CCS Concepts 
Applied computing -> Health care information systems; Health informatics; Human-centered computing -> Visual analytics; Information visualization

Screenshot of the augmented  ow sheet timeline.

Augmenting EHR Interfaces for Enhanced Nurse Communication and Decision Making

This paper introduces a novel web application that visualizes dark matter halo merger trees and their evolution through space and
time. Our application enables users to interact with individual halos within these trees in order to perform a range of visual analysis
tasks, including: identifying the substructure and superstructure of the halos; observing the movement of halos across a specified range of time steps; and comparing the branching characteristics of multiple trees. Central to our application is the ability to navigate the halos by interactively &ldquo:jumping&rdquo; from tree to tree. By clearly marking halos that have &ldquo;tributaries&rdquo; - that is, halos that split off into multiple halos or that merge with one or more halos - we make it easy for the user to traverse the complex structure of the universe. Our application is publicly available online and runs at interactive rates on the browser.

A screen capture of our web application showing a complete dark matter halo merger tree.

Halos in a Dark Sky: Interactively Exploring the Structure of Dark Matter Halo Merger Trees

We present the design and implementation of a visual mining and analysis tool to help identify protein mutations across family structural models, and to help discover the effect of these mutations on protein function. We follow a client-server approach in which distributed data sources for 3D structure and non-spatial sequence information are seamlessly integrated into a common visual interface. Multiple linked views and a computational backbone allow comparison at the molecular and atomic levels, while a trend-image visual abstraction allows for the sorting and mining of large collections of sequences and of their residues. We evaluate our tool on the triosephosphate isomerase (TIM) family structural models and sequence data, and show that our tool provides an effective, scalable way to navigate a family of proteins, as well as a means to inspect the structure and sequence of individual proteins. 
Keywords: Molecular Sequence Analysis, Molecular Structural Biology, Computational Proteomics

scTIM/dTIM comparison.

Fixing TIM: Identifying Functional Mutations in Protein Families through the Interactive Exploration of Sequence and Structural Data

Simulation and modeling of turbulent flow, and of turbulent reacting flow in particular, involve solving for and analyzing time-dependent and spatially dense tensor quantities, such as turbulent stress tensors. The interactive visual exploration of these tensor quantities can effectively steer the computational modeling of combustion systems. In this article, the authors analyze the challenges in dense symmetric-tensor visualization as applied to turbulent combustion calculation; most notable among these challenges are the dataset size and density. They analyze, together with domain experts, the feasibility of using several established tensor visualization techniques in this application domain. They further examine and propose visual descriptors for volume rendering of the data. Of these novel descriptors, one is a density-gradient descriptor which results in Schlieren-style images, and another one is a classification descriptor inspired by machine-learningtechniques. The result is a hybrid visual analysis tool to be utilized in the debugging, benchmarking and verification of models and solutions in turbulent combustion. The authors demonstrate this analysis tool on two example configurations, report feedback from combustion researchers, and summarize the design lessons learned. 
&copy; 2016 Society for Imaging Science and Technology. 
[DOI: 10.2352/J.ImagingSci.Technol.2016.60.1.010404]

Volume rendering of divergence of the temporal mixing layer dataset, and color transfer function.

Visual Descriptors for Dense Tensor Fields in Computational Turbulent Combustion: A Case Study

Simulation and modeling of turbulent flow involve solving for and analyzing time-dependent and spatially dense tensor quantities, such as turbulent stress tensors. The interactive visual exploration of these tensor quantities can effectively steer the computational modeling of combustion systems. At the same time, visualizing such data poses significant challenges in terms of clutter, occlusion
and navigation. We present an interactive framework which combines a detailed 3D inspection view based on volume rendering with glyph-based representations - used as 2D probes -, while leveraging interactive filtering and flow salience cues to clarify the structure of the tensor datasets. The result is a preliminary visual analysis tool to be utilized in debugging, benchmarking, and verification of models and solutions in turbulent combustion. We demonstrate this analysis tool on two example configurations and report feedback
from combustion researchers, in particular with respect to the merits of the visualization techniques employed. 
Keywords: Tensor Visualization, Application, Strain Tensor, Computational Turbulent Combustion.

Combined tensor visualization techniques in computational turbulent combustion.

Tensor Visualization in Computational Turbulent Combustion: A Case Study

In visualization of tensor data associated with turbulent combustion simulations, the datasets we have to process and visualize are typically enormous in size and continue to grow exponentially. The large amount of data may severely affect the manipulating speed, thus posing a major challenge to interactive visualization. Even worse, tensor datasets tend to be very dense leading to clutter and occlusion problems. To address these issues, feature extraction is an emerging method. Typically, only a small percentage of data is of interest, thus making the effective visualization of very large datasets possible. Feature extraction also helps the users highlight and focus on regions of interest. 
We introduce an approach for the segmentation, visualization and tracking of regions of interest in large scale tensor field datasets generated by computational turbulent combustion simulations. We use canopy clustering followed by a K-means algorithm to partition and cluster the tensor field components. The resulting clusters are tracked through multiple timesteps. Interactive, hardware-accelerated volume renderings are generated using the cluster indices. Results on two rich datasets show this approach can assist in the visual analysis of combustion tensor fields. 
A great deal of research has been conducted on the problems of feature extraction and tracking, as well as in characterizing, detecting and visualizing regions of interes. The method we present herein is novel in that it integrates machine learning with visualization for extracting and clustering regions of interest. It is thus a promising approach to apply to very large flow datasets.

Tensor-based region of interest tracking for a mixing layer dataset.

A Novel Method for Tracking Tensor-based Regions of Interest in Large-Scale, Spatially-Dense Turbulent Combustion Data

Simulation and modeling of turbulent flow, and of turbulent reacting flow in particular, involves solving for and analyzing time-dependent and spatially dense tensor quantities, such as turbulent stress tensors. The interactive visual exploration of these tensor quantities can effectively steer the computational modeling of combustion systems. In this chapter, we discuss the challenges in dense symmetric tensor visualization applied to turbulent combustion calculation, and analyze the feasibility of using several established tensor visualization techniques in the context of exploring space-time relationships in computationally-simulated combustion tensor data. To tackle the pervasive problems of occlusion and clutter, we propose a solution combining techniques from information and scientific visualization. Specifically, the proposed solution combines a detailed 3D inspection view based on volume rendering with glyph-based representations - used as 2D probes -, while leveraging interactive filtering and flow salience cues to clarify the structure of the tensor datasets. Side-by-side views of multiple timesteps facilitate the analysis of time-space relationships. The resulting prototype enables an analysis style based on the overview first, zoom and filter, then details on demand paradigm originally proposed in information visualization. The result is a visual analysis tool to be utilized in debugging, benchmarking, and verification of models and solutions in turbulent combustion. We demonstrate this analysis tool on three example configurations and report feedback from combustion researchers.

DNS (left), LES (middle) and RANS (right) predictions of a turbulent jet. LES requires less computational effort than DNS, while delivering more detail than the inexpensive RANS.

Interactive Exploration of Stress Tensors Used in Computational Turbulent Combustion

Simulation and modeling of turbulent flow, and of turbulent reacting flow in particular, involves solving for and analyzing time-dependent and spatially dense tensor quantities, such as turbulent stress tensors. The interactive visual exploration of these tensor quantities can effectively steer the computational modeling of combustion systems. In this work we present an integrated, hybrid tensor visualization tool. Volume renderings of the 3D quantities reveal the structure of the dataset, allowing users to identify regions of interest. The regions of interest can then be further explored using the 2D glyph-based representations. Finally, streamlines are also provided to help reveal the structure of the flow through the region of interest. In order to facilitate time-space analysis, we provide a small-multiples interface: users can compare thumbnail, low resolution representations of different time steps and then focus on one time step to view in detail. We demonstrate our application on several turbulent configurations, and show this approach can successfully capture and highlight numerical artifacts in the underlying computational model.

Visualization of Tensor Quantities Used in Computational Turbulent Combustion

In this paper, we develop and implement approaches to user-driven predictive visual analytics on multivariate, spatio-temporal incident reporting data on the Lords Resistance Army (LRA) activity in Central Africa. We concentrate on specific predictive questions that pivot on LRA movement and cause-and-effect patterns. Our approach represents the output of simple models, such as a movement model and a local-related-incidents model. In addition, we provide users with interactive features to explore, which allows for the isolation of appropriate subsets of the data and visually separates signal from noise when addressing predictive questions. We implement this approach and use it to develop predictive hypotheses around variations in movement patterns of the LRA and to identify potential cause-and-effect patterns around the activity of commanders and solider defections. 
Index Terms: Visual analytics, prediction, spatio-temporal data

User-Driven Predictive Visual Analytics on Multivariate, Spatio-Temporal Incident Reports

A common strategy for encoding multidimensional data for visual analysis is to use dimensionality reduction techniques that project data from higher dimensions onto a lower-dimensional space. This article examines the use of motion to retain an accurate representation of the point density of clusters that might otherwise be lost when a multidimensional dataset is projected into a two-dimensional space. Specifically, we consider different types of density-based motion, where the magnitude of the motion is directly related to the density of the clusters. We investigate how users interpret motion in two-dimensional scatterplots and whether or not they are able to effectively interpret the point density of the clusters through motion. We conducted a series of user studies with both synthetic and real-world datasets to explore how motion can help users in completing various multidimensional data analysis tasks. Our findings indicate that for some tasks, motion outperforms the static scatterplots; circular path motions in particular give significantly better results compared to the other motions. We also found that users were easily able to distinguish clusters with different densities as long the magnitudes of motion were above a particular threshold. Our results indicate that incorporating density-based motion into visualization analytics systems effectively enables the exploration and analysis of multidimensional datasets. 
Keywords: Visual analytics, visual clustering, user study, spatial layout, scatterplots, multidimensional visualization,
perception

Examples from our user study in which a user is asked to detect a cluster with a different magnitude of motion.

Density-based motion

Visualizing and analyzing the relationships between taxonomic entities represented in multiple input classifications is both challenging and required due to recurrent new discoveries and inferences of taxa and their phylogenetic relationships. Despite the availability of numerous visualization techniques, the large size of hierarchical classifications and complex relations between taxonomic entities generated during a multi-taxonomy alignment process requires new visualizations. This paper introduces ProvenanceMatrix, a novel tool allowing end users (taxonomists, ecologists, phylogeneticists) to explore and comprehend the outcomes of taxonomic alignments. We illustrate the use of ProvenanceMatrix through examples using taxonomic classifications of various sizes, from a few to hundreds of taxonomic entities and hundreds of thousands of relationships. 
Keywords: Taxonomic classification, multi-taxonomy alignment, phylogenetic relationship, matrix representation, glyph-based visualization

Abstract toolkit input and output example rendered with GraphViz.

ProvenanceMatrix: A Visualization Tool for Multi-Taxonomy Alignments

This paper explores the specialized nature of research-oriented web applications that enable interactions with and the visual analysis of &ldquo;Big Data,&rdquo; i.e., large, heterogeneous scientific datasets. We introduce a pragmatic methodology for the design and evaluation of scientific workflows in research-oriented web applications. Through an in-depth usability study of the CoGe web application, a system that provides a rich set of tools for exploring genomic datasets, we demonstrate: how to identify bottlenecks in multi-step tasks; how to analyze these bottlenecks in order to provide effective solutions for improving user experience; and how these solutions may more generally apply to similar research-oriented websites in other scientific domains that also enable scientific workflows. Specifically, we provide details regarding: our user interviews, the visualization system we created to analyze complex tasks associated with scientific workflows, and how this analysis directly leads to suggestions for improvements in the current implementation of the CoGe web application. A follow-up study was carried out which indicates that our suggestions improved the ability of CoGe users to navigate and complete custom workflows, leading us to believe that our approach could also be applied to other research-oriented web applications that utilize scientific datasets. 
Keywords: Scientific workflows, Big Data, user evaluation, visual analytics.

Comparison of typical workflow for user completion of Task.

Designing and Evaluating Scientific Workflows for Big Data Interactions

This paper describes the creation of SwarmVision, a system of autonomous robotic cameras that functions both as the basis for an art installation and as an instrument for generating novel engineering research. Through a series of interviews with engineers who examined the project, we illustrate how experimentation with the instrument could lead to potential new research directions in computer vision, machine learning, swarm robotics, remote collaboration, and visualization. This suggests that an unstructured and aesthetic approach to research can inform and inspire new engineering research directions, within and beyond the scope of this particular project. 
Keywords: Media arts methodologies, computational photography, swarm robotics, autonomous robotic cameras, arts and engineering.

Camera view of autonomous image capture process viewed in the 3D virtual scene overview.

Generation of Engineering Research Directions through Artistic Process

EVL PhD candidate Chihua Ma presents her preliminary research in visual analytics for biological networks. 
Monday, June 13, 2016 
12:30 PM - 2:00 PM 
EVL Cyber-Commons, 2032 ERF 
Abstract: 
Biological systems are often modeled as networks in which vertices denote individual bio-units, and edges represent interactions between vertices, such as protein-protein interaction networks, gene regulatory networks, and neural networks. Biological networks feature spatial features, e.g. coordinates in the brain, and nonspatial features, e.g. the network structure. They change dynamically, e.g. brain activation. They are analyzed and explored at multiple levels. For example, brain activation data may range from the macroscale level to the microscale level. Last but not least, modeling these networks is often done over multiple examples or with different models. 
This wealth of data, features, models, and potential hypotheses and experiments exceeds the analytical capabilities of machines. Visualization provides an effective way to help biologists understand, communicate, and gain insight into their biological data through visual analysis and exploration. 
I have contributed to a survey of spatial-nonspatial integration in biology; designed several novel multi-scale visual encodings; and completed three applications that partially support the biological problems with the combination of those three challenges. In the future work, I will contribute to a taxonomy, design novel multi-scale visual representations and develop a visual approach that can fully support the big challenge.

Visual Analysis Techniques for Spatial-Nonspatial, Dynamic, Multi-Scale and Multi-Run Biological Networks

The visualization of aleatoric uncertainty in ensemble simulations - i.e. the study of high concentrations in mixtures - provides many challenges due to the size and dimensionality of the data sets. EVL researchers have developed a web-based application designed to locate and track areas of high concentration in salt-solution simulations. 
The goal is to learn more about the behavior of Viscous Fingers, or contiguous areas of high concentration of salt within a cylinder of pure water. The application allows viewers to explore 3-D ensemble simulation data using both 2-D vertical slices of the cylindrical data to get a more detailed view of the data in a specific time step, as well as a feature tracking merge tree to better understand the evolution of these Viscous Fingers over the course of a simulation run.

Screen Shot of Viscous Fingers Visualization Application

Interactive Exploration and Tracking of Viscous Fingers in Large-Scale Ensemble Simulations

Images from two articles by EVL&rsquo;ers made the cover of the January/February issue of JIST (Journal of Imaging Science and Technology)! 
COVER - TOP IMAGES (white background): 
Chihua Ma, Angus Forbes, Daniel Llano, Tanya Berger-Wolf, Robert V. Kenyon, &ldquo;SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks,&rdquo; Journal of Imaging Science and Technology, 60(1), January/February 2016, pp. 010405-1 - 010405-13, DOI: <a href="http://dx.doi.org/10.2352/J.ImagingSci.Technol.2016.60.1.010405">http://dx.doi.org/10.2352/J.ImagingSci.Technol.2016.60.1.010405</a> 
COVER - BOTTOM IMAGES (black backgrounds): 
G. Elisabeta Marai, Timothy Luciani, Adrian Maries, S. Levent Yilmaz, Mehdi B Nik, &ldquo;Visual Descriptors for Dense Tensor Fields in Computational Turbulent Combustion: A Case Study,&rdquo; Journal of Imaging Science and Technology, 60(1), January/February 2016, pp.010404-1 - 010404-11, DOI: <a href="http://dx.doi.org/10.2352/J.ImagingSci.Technol.2016.60.1.010404">http://dx.doi.org/10.2352/J.ImagingSci.Technol.2016.60.1.010404</a>

Cover art of JIST January/February 2016 issue features EVL research

The thesis work presented here focuses on the optimization of the similarity computation among nodes for spatio-temporal networks. The study case in this work is the computation of spatio-temporal networks on brain. The aim of the first part of the work presented is to built a GPU-based computation system able to speed up the networks definition. In the second part of the work we developed an interactive tool to visualize the networks computed using the GPU-based method developed in this first part. 
Nowadays, with the increase of computational analysis in sciences such as biology and neuroscience, the computational aspect is the most challenging one. Scientists need tools able to process large amounts of data simultaneously. Previously, scientific computations were performed using clusters of computers, but this type of infrastructure is very expensive and complex to build. The work in this thesis is part of a greater project that has the aim to apply spatio-temporal networks inference techniques to perform network analysis studies in different fields. One of the problems of spatio-temporal network applications is the computational time, and it becomes impractical to keep developing studies when it takes a long time to analyze and compute the results. We aim to help the researchers to get results in a reasonable amount of time, so they can be focused more on their studies. There are many ways to speed up the computation process, one of these is to exploit the paradigm of parallel computation using Graphic Processor Unit (GPU). GPUs represent a low-cost solution to this problem since the level of parallelization they have is really high and GPUs hardware match exactly the requirements for the problem addressed. With the first part of this thesis work we present a GPU solution and we evaluate the performance of spatio-temporal networks definition. 
The second part of the thesis work uses the results computed in the first part to propose an interactive visualization tool for the spatio-temporal networks computed. With a decrease in computation time, it is easier to achieve a comparison between the results. The proposed visualization tool allows for the changing of parameters at run-time and visualizing the differences in the resulting networks. This process is made possible thanks to a great work of pre-computation of all the data that must to be visualized using the GPU-based system developed. This second part of the work goals to improve the understanding of the results computed by the algorithm. In this study, we focus on brain network computation and visualization. 
We mentioned that this thesis work is composed by two different parts. One of them is used to compute the similarity measure needed to define the DN. While, the other aims to visualize interactively the DN computed, leaving one degree of freedom to the
final user. One of the goals of this work is the creation of a pipeline that goes from the collected data to the visualization of the result, which in our case are the networks computed previously. In fact the two piece of software developed are part of the pipeline that defines the framework we present. This framework want to avoid the dependability problem given by that the different software are not able to communicate among each others.

Network comparison, it shows the comparison between two different data session loaded in the system, each one is represented by a color in the network.

GPU Acceleration and Interactive Visualization for Spatio-Temporal Networks

Monday, May 2, 2016, 9:30 AM 
Room 1000, Science & Engineering Office Building, UIC 
UIC Computer Science and EVL exchange student from the Politecnico di Milano in Italy, Andrea Purgato presents his thesis research that explores the optimization of the similarity computation among nodes for spatio-temporal networks. 
This research is divided into two parts. The first aspect is to develop a GPU-based computation system to speed up the networks definition. The second uses the GPU-based method developed initially to develop an interactive visualization tool focused specifically on brain network visualizations.

GPU Acceleration and Interactive Visualization for Spatio-Temporal Network

Brain network visualization interface showing networked communities plotted.

Andrea Purgato Thesis Presentation: &ldquo;GPU Acceleration and Interactive Visualization for Spatio-Temporal Network&ldquo;

While most academic conferences attract people who want to learn, the annual IEEE Visualization (VIS) conference attracts people who want to be challenged. Throughout the year, the conference sponsors several contests - to encourage the best visualizations and designs of complex datasets. Awards and recognition are announced at the annual conference, which is taking place this week in Baltimore, Maryland. 
Faculty and students of the Electronic Visualization Laboratory (EVL) at the University of Illinois at Chicago were surprised and elated to learn they won Honorable Mention in the IEEE Visualization and Graphics Technical Committee&rsquo;s (VGTC) third International Data Visualization Contest, organized by the IEEE Visualization Pioneers Group (VPG). This year, the contest was done in collaboration with ACT, Inc., the company that administers the standardized test for college readiness assessment. 
Early in the Fall 2016 semester, UIC Computer Science (CS) associate professor G. Elisabeta Marai gave students in her CS 526 Interactive Scientific Visualization course a design assignment based on the IEEE VGTC VPG Data Visualization Contest. The students - including Timothy Luciani, Juan Trelles Trabucco, Chihua Ma, Andrew Burks, Manu Thomas, Krishna Bharadwaj, Shiwangi Singh, Peter Hanula, and Lei Di - spent several weeks designing, implementing, and integrating pieces for a group submission to the contest. Serendipitously, their visualization effort, called &ldquo;Multi-Scale Voronoi-Based ACT Assessment&rdquo; was recognized as one of the winning entries by the judges. The group was honored at the VPG Reception at VIS 2016. 
ACT operates nearly 9,000 testing centers annually to deliver the ACT Assessment test to over two million high school students on six different Saturdays each year as part of its open, &ldquo;National&rdquo; program. Use of the test data by schools and government agencies has grown significantly in recent years, and is increasing the demands on the ACT network and test seat availability. Thus, ACT seeks to use data it collects to better manage its system and its operating costs. 
The 2016 challenge was to develop a static visual representation that shows how geographic and seasonal changes in the availability of the ACT test affects nearby or adjacent testing sites, by moving students or assessments, changing dates, or some other strategy. For example, how does the addition or subtraction of a testing center affect demand in neighboring zip codes, or what is the optimal test center network in a particular season? 
The students who worked on the winning entry are UIC CS PhD, MS and BS students, many of whom have EVL Research Assistantships or Research Experiences for Undergraduate (REU) support. 
<a href="http://vacommunity.org/ieeevpg/viscontest/2016/">IEEE VGTC VPG third International Data Visualization Contest information.</a> 
# # # # # # 
IMAGE CAPTION AND CREDITS 
TOP LEFT: Choropleth maps showing national-level costs and utilization. The large green map shows aggregate expenses (2012-2015). California, Texas, and Florida have the three highest expenses. The small multiple below shows the expense distribution per year (no significant variation). The purple maps show, similarly, the percent utilization (Assigned/Capacity), and a reasonably fair distribution nationwide. 
BOTTOM: Average bar charts. Two bar charts display the average expenses and utilization per state (2012-2015). The charts provide a quantitative comparison across states and complement the choropleths. 
TOP MIDDLE: Voronoi visualization of test center distribution at regional level (Illinois). Voronoi diagrams partition the region based on distances to test centers. The Voronoi cell intensity is mapped to Assigned/Capacity. The darker the cell, the higher demand in that region. The partially filled red circle in each cell double-encodes utilization. Circle size encodes the Center capacity, and the filled area shows the assigned number. Orange markers show population sizes per Zipcode. The region with the most test centers in Illinois is the greater Chicago area.K 
TOP MID-RIGHT: Side-by-side Voronoi analysis of center addition (Cook County). Two side-by-side Voronoi maps show county-level changes before and after the addition of two new test centers (yellow). The saturation of cells with a yellow boundary captures the effect over nearby centers. Lighter Voronoi cells in the South Side of Chicago indicate lower participation in those neighborhoods. 
TOP RIGHT: Seasonal Optimized Network. Four maps show seasonal population density per Zipcode (Chicago region). Partially filled red glyphs encode Centers and show the Assigned/Capacity per season. The Center network shown is a custom optimized network in which under-utilized Centers were iteratively removed and the nearest Center capacity was increased. Summer and Fall are popular in Chicago, and have dense Center networks.

IEEE VIS'16 VGTC VPC Data Visualization Contest winners - Juan Trelles, Chihua Ma, Manu Thomas, Timothy Luciani, G.E. Marai, Shiwangi Singh, Lei Di, Peter Hanula, Krishna Bharadwaj, Andrew Burks; EVL/UIC

UIC/EVL Receives Honorable Mention in IEEE VIS 2016’s VGTC VPG Data Visualization Contest

MS Graduate 2018

Manu Mathew Thomas

Newcity magazine is Chicago&rsquo;s only locally owned and operated cultural weekly, and has been covering Chicago&rsquo;s local scene for more than 25 years. It&rsquo;s current &ldquo;Best of Chicago&rdquo; issue just came out (Volume 31, No. 1359). The section on City Life lists many best things about Chicago, including &ldquo;Best place to get your geek on.&rdquo; Um, that would be the Electronic Visualization Laboratory at the University of Illinois at Chicago! The article is reproduced below. 
* * * * * * * * * * 
Best place to get your geek on 
The Electronic Visualization Laboratory (EVL) at UIC 
Cutting-edge research meets a massive wall made out of small screens that would make any hardcore videogame fan scream and a literal cave that opens up a highly immersive, surround-screen, surround-sound, virtual reality environment at the Electronic Visualization Laboratory (EVL) at the University of Illinois at Chicago. The lab brings together academics, designers, engineers, artists and mad scientists using technologies that bend reality off into unimaginable directions. EVL is the place where visualization, virtual reality and real-time interactive computer graphics are born. As if that were not enough, it was here that the computer graphics for the first &ldquo;Star Wars&rdquo; film were created in 1976. How cool is that? 
UIC College of Engineering, Department of Computer Science, 842 W. Taylor 
Best of Chicago 2016

Best of Chicago 2016

Newcity magazine rates UIC/EVL as &ldquo;Best place to get your geek on&rdquo;

We describe the lessons learned from three recent Immersive Analytics projects which take place at the Electronic Visualization Lab. These successful collaborative projects use the CAVE2 immersive hybrid environment. All three projects visualize multifaceted scientific data and benefit from interdisciplinary collaborations with experts across application domains. We further outline the challenges and opportunities ahead for the field of Immersive Analytics. 
Index Terms: K.6.1 [Immersive Analytics]: Virtual Reality-Interdisciplinary collaborations; K.7.m [Technology]: Displays-CAVE2

Young researchers examine, as a group, large scale cosmology data in CAVE2.

Interdisciplinary Immersive Analytics at the Electronic Visualization Laboratory: Lessons Learned and Upcoming Challenges

In this paper we introduce TimeArcs, a novel visualization technique for representing dynamic relationships between entities in a network. Force-directed layouts provide a way to highlight related entities by positioning them near to each other. Entities are brought closer to each other (forming clusters) by forces applied on nodes and connections between nodes. In many application domains, relationships between entities are not temporally stable, which means that cluster structures and cluster memberships also may vary across time. Our approach merges multiple force-directed layouts at different time points into a single comprehensive visualization that provides a big picture overview of the most significant clusters within a user-defined period of time. TimeArcs also supports a range of interactive features, such as allowing users to drill-down in order to see details about a particular cluster. To highlight the benefits of this technique, we demonstrate its application to various datasets, including the IMDB co-star network, a dataset showing conflicting evidences within biomedical literature of protein interactions, and collocated popular phrases obtained from political blogs. 
Categories and Subject Descriptors (according to ACM CCS): H.5.2 [Information Interfaces and Presentation]: User
Interfaces-Graphical user interfaces

TimeArcs visualization for interactions around OPSD protein. (1) and (2) are conflicting evidences of OPSD and K+ interaction in 2003 and 2012 publications.

TimeArcs: Visualizing Fluctuations in Dynamic Networks

Adjacency matrices can be a useful way to visualize dense networks. However, they do not scale well as the network size increases due to limited screen space, especially when the number of rows and columns exceeds the pixel height and width of the screen. We introduce a new scalable technique, MultiLayerMatrix, to visualize very large matrices by breaking them into multiple layers. In our technique, the top layer shows the relationships between different groups of clustered data while each sub-layer shows the relationships between nodes in each group as needed. This process can be applied iteratively to create multiple sub-layers for very large datasets. We illustrate the usefulness of MultiLayerMatrix by applying it to a network representing similarity measures between 2,048 characters in the Asteraceae taxonomy, a rich dataset that describes characteristics of species of flowering plants.We also discuss the scalability of our technique by investigating its effectiveness on a large synthetic dataset with 20,000 columns by 20,000 rows.

Visualizing 2048 nodes in a regular adjacency matrix (left) and in a MultiLayerMatrix of two layers: The middle panel shows the first layer, and the right panel shows an example of the second layer.

MultiLayerMatrix: Visualizing Large Taxonomic Datasets

Node-link diagrams are widely used for visualizing relational data in a wide range of fields. However, in many situations it is useful to provide set membership information for elements in networks. We present BranchingSets, an interactive visualization technique that uses visual encodings similar to Kelp Diagrams in order to augment traditional node-link diagrams with information about the categories that both nodes and links belong to. BranchingSets introduces novel user-driven methods to procedurally navigate the graph topology and to interactively inspect complex, hierarchical data associated with individual nodes. Results indicate that users find the technique engaging and easy to use. This is further confirmed by a quantitative study that compares the effectiveness of the visual encodings used in BranchingSets to other techniques for displaying set membership within node-link diagrams, finding our technique more accurate and more efficient for facilitating interactive queries on networks containing nodes that belong to multiple sets.

A prototype application that uses the BranchingSets technique to facilitate the navigation of a complex, hierarchical biological pathway dataset where both nodes and links are members of one or more sets.

BranchingSets: Interactively Visualizing Categories on Node-Link Diagrams

UIC Computer Science Associate Professor and Electronic Visualization Laboratory faculty member, G. Elisabeta Marai with collaborators from the University of Texas, University of Iowa, and University of Minnesota has been awarded an NIH R01 from the National Cancer Institute. 
&ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; will develop a computational methodology that supports the construction of a validated precision model of patient-specific outcomes for head and neck cancers, based on demographics, toxicity, and complex imaging data. The methodology will be the first in the field to include both complex imaging and non-imaging data, while taking into account large-scale biological and clinical correlates. A web-based environment will further allow practitioners to interactively explore and communicate the model mechanics and outcomes to stakeholders. The approach is innovative through its leverage of big imaging data repositories and through its unique blend of computational modeling principles from bioengineering, statistics, and computer science (medical imaging and visualization).

CS/EVL Associate Professor Marai awarded NIH R01 from the National Cancer Institute

This article presents the main features and components of iCardia - an innovative mHealth platform designed to support remote monitoring and health coaching of cardiac rehabilitation (CR) patients, through Fitbit wearable sensor devices, smartphones, and personalized SMS text messages. The design and development of iCardia were based on an iterative, user-centered design process and an open-service architecture to ensure rapid scalability and adherence to evidence-based guidelines for easier transition into clinical practice. iCardia has the potential to enable a paradigm shift towards a collaborative CR environment that utilizes mHealth technologies to engage patients to more effectively self-manage their cardiovascular disease.

iCardia platform components and architecture

Development of an innovative mHealth platform for remote physical activity monitoring and health coaching of cardiac rehabilitation patients

&ldquo;SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks,&rdquo; authored by Ma, C., Forbes, A. G., Llano, D. A., Berger-Wolf, T., Kenyon, R. V. was recognized by the Society for Imaging Sciences and Technology as the recipient of the 2017 Charles E. Ives Journal Award. 
The Charles E. Ives Award is given in recognition of an outstanding contribution in the area of basic or applied science or engineering published in a Society journal (JIST or JEI) during the preceding calendar year.

Flow diagram: (a) mouse brain, (b) camera, (c) time-series imaging data of mouse brain slices, (d) generate dynamic communities using DNA and (e) present the analysis results using the SwordPlots visualization.

EVL PhD Candidate Chihua Ma Receives Charles E. Ives Journal Award

EVL PhD Candidate, Timothy Luciani presents his research &ldquo;Contextual Similarity Abstraction Techniques for Spatial Data Analysis&rdquo; during his preliminary exam. 
Date: Thursday July 13, 2017 
Time: 10:00am 
Room: ERF 2068 (Cybercommons) 
Committee: Dr. G. Elisabeta Marai (Chair), Dr. Robert Kenyon, Dr. Andrew Johnson, Dr. Angus Forbes, Dr. Klaus Mueller (Stony Brook University, NY) 
Abstract: 
Comparative analysis of modern large-scale datasets is a core task in scientific visualization. This task entails identifying both global similarities among entire data collections, and local similarities between smaller data subsets to help provide critical insights into data trends and potentially lead to classification, validation and discovery. To this day, Shneiderman&rsquo;s visual information-seeking mantra remains the most common approach to designing comparative visualization applications: Overview first, zoom and filter, then details on demand. However, for large-scale datasets, this mantra becomes infeasible due to the high volume (number of entries and dimensionality) of the data. Van Ham and Perer explore an alternative, context-based model that emphasizes the use of contextual information as a means of data reduction: Search, show context, expand on demand. While powerful, this paradigm has not been well studied outside the domain of large-scale, dense graphs. Specifically, little is known on how well this approach transfers to domain-specific, datasets that are heterogeneous, spatially and statistically dense yet locally sparse, and possess spatial/non-spatial properties. 
To help explore this problem space, I propose a set of comparative analysis techniques which extend the Van Ham and Perer paradigm to spatial, scientific data. These techniques employ a blending of derived, quantitative similarity metrics and high-density visual abstractions - implemented through various integrated systems - to handle the inherent global data density and local attribute sparsity found in these datasets. Each of these approaches improves the likelihood of discovering patterns and similarities by first limiting the data to a user-defined subset before providing abstracted, contextual overviews.

&ldquo;Contextual Similarity Abstraction Techniques for Spatial Data Analysis&rdquo;

One of the significant challenges of care transitions in Intensive Care Units (ICUs) is the lack of effective support tools for outgoing
clinicians to find, filter, organize, and annotate information that can be effectively handed off to the incoming team. We present a large display interactive multivariate visual approach, aimed towards supporting clinicians during the transition of care. We first provide a characterization of the problem domain in terms of data and tasks, based on an observation session at the University of Illinois Hospital, and on interviews with several biomedical researchers and ICU clinicians. Informed by this experience, we design a scalable, interactive visual approach that supports both overview and detail views of ICU patient data, as well as anomaly detection, comparison, and annotation of the data. We demonstrate a large-display implementation of the visualization on an existing anonymized ICU dataset. Feedback from domain experts indicates this approach successfully meets the requirements of effective care transitions. 
Index Terms: ICU care transition, large display visualization, shared cognition, collaborative decision making

(Left) Two colocated testers establish a common information space using Echo on a tiled large display wall that runs SAGE2 middleware. (Right) A group of testers run Echo synchronously on a large display screen, on a laptop and on a tablet.

Echo: A Large Display Interactive Visualization of ICU Data for Effective Care Handoffs

Background: Visualizing the complex probability landscape of stochastic gene regulatory networks can further
biologists&rsquo; understanding of phenotypic behavior associated with specific genes. 
Results: We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. 
Conclusions: We demonstrate the effectiveness of the tool through two case studies on the computed probabilisti landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases. 
Keywords: Systems biology, Feature detection and tracking, Network analysis, Modelling/Simulation

The PRODIGEN interface consists of several visual components.

PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space

Mini Challenge 2 of the VAST Challenge 2017 focused on a small industrial area south of the fictional Mistford preserve, specifically around four manufacturing factories. Our main goal was to develop a visual analytics tool to explore the spatio-temporal chemical readings and wind data. Specifically, we wanted to determine which factories were responsible for emitting which chemicals and to determine the performance of the nine sensors in the area. In order to help achieve this goal, we developed a web-based application that utilizes interactive visualizations and path line analysis for revealing sensor errors and chemical reading spikes, along with pinpointing the possible sources of chemical reading spikes.

EVL Project Team (from left to right): Andrew Burks, Joe Borowicz, Elisabeta Marai (faculty advisor), Joshua Castor and Manu Thomas

MC2 - Mining Factory Pollution Data through a Spatial-Nonspatial Flow Approach

Our web-based image analysis tool for the VAST 2017 Mini-Challenge 3 combines small multiple views of satellite images, linked semantic zooming and image intensity histograms, along with filter controls. The resulting tool allow users to interactively analyze spatio-temporal changes in the preserve area.

EVL Project Team (from left to right): Bart Kupiec, Andrew Burks, Elisabeta Marai (faculty advisor) and Vijayraj Mahida

MC3 - A Web-Based Interactive Image Explorer for Temporal Analysis of Satellite Images

Research Experience Undergraduate, BS 2018

Joe Borowicz

SEPTEMBER 11, 2017 - UIC Computer Science (CS) Associate Professor G. Elisabeta (Liz) Marai loves to inspire and educate next-generation computer scientists. For several years now, she has selected the best and brightest undergraduates from her Fall and Spring courses and, pending financial support, offers them both summer research experiences and extracurricular activities to accelerate their knowledge of visualization and visual analytics technologies and techniques. 
Earlier this year, Marai reached out to CS faculty, and particularly CS faculty affiliated with the Electronic Visualization Laboratory (EVL), to ask if they would join her in hiring her undergrads on grants, with funding provided by the NSF Research Experiences for Undergraduates (REU) program or by private donors. While the students would be paid to work on a variety of projects, Marai would also provide them with extracurricular work - participating in the 12th Visual Analytics Science and Technology (VAST) Challenge, hosted annually by the international IEEE Visualization (VIS) conference. 
Months prior to the IEEE VIS conference, the VAST Challenge publishes several problems (a Grand Challenge and three Mini-Challenges) around one theme. This year, it focused on a fictitious mid-size city with a small industrial area located near a wildlife preserve, where it was discovered that the population of a specific local bird was decreasing. Each of the VAST problems provided different datasets, with different questions about &ldquo;who-what-where-why-when–how&rdquo;. For each problem, the goal was to design and develop specific visual analytics tools to help the local ornithologist determine what was happening to the birds. 
How do undergrad sophomores and juniors deal with such a competition? They work together in teams, and they learn a lot about visualization, visual analytics, programming, and web design and development. And, they are mentored by Marai&rsquo;s PhD/MS students, as well as other EVL faculty, staff and students. 
This year&rsquo;s VAST Challenge received 56 submissions from individuals and teams in industry, government, and academia. EVL student teams submitted three entries, one for each Mini-Challenge; two EVL submissions received Honorable Mention awards and one was well reviewed but did not receive an award. Given these submissions were led by undergraduates, most of whom had never done visualization before (most were students from Marai&rsquo;s CS342 software design course), that&rsquo;s a huge accomplishment! 
All the student teams wrote two-page papers about their entries that will be published in the IEEE VIS 2017 electronic conference proceedings. Marai will fund two students to attend this year’s IEEE VIS conference, to be held October 1-6, 2017 in Phoenix, AZ, to present their solutions, and to receive their Honorable Mention certificates on behalf of their teammates. 
 
# # # # # # 
 
Here is a summary of the three projects. 
Mini-Challenge #2: Honorable Mention for Clarity in Visual Communication 
Project: &ldquo;Mining Factory Pollution Data through a Spatial-Nonspatial Flow Approach&rdquo; 
UIC Participants: Undergraduates Joshua Castor, Joseph Borowicz and Andrew Burks; graduate students Manu Thomas and Timothy Luciani; and, faculty mentor Liz Marai. 
The dataset provided several months of meteorological data (wind speed and direction) and chemical data emitted by four industrial factories and captured by nine sensing stations. To explore the spatio-temporal chemical readings and wind data, specifically which factories emitted what chemicals and how the nine sensors in the area were performing, the team developed a web-based analytics tool with interactive visualizations and path line analysis to reveal sensor errors and chemical reading spikes, as well as pinpoint possible sources of chemical reading spikes. The goal was to help the local ornithologist determine whether or not the factories were compliant with environmental regulations. 
Mini-Challenge #3: Honorable Mention for Good Interactive Image Explorer for Temporal Analysis 
Project: &ldquo;Web-Based Interactive Image Explorer for Temporal Analysis of Satellite Images&rdquo; 
UIC Participants: Undergraduates Vijayraj Mahida, Bartosz Kupiec and Andrew Burks; graduate student Timothy Luciani; and, faculty mentor Liz Marai. 
The dataset provided satellite imaging data from commercial multi-spectral imagers that routinely cover the nature preserve every few weeks. The team developed a web-based image analysis tool that combined small, multiple views of satellite images, linked semantic zooming and image intensity histograms, along with filter controls. The goal was to help the local ornithologist interactively analyze spatio-temporal changes to understand the health of the wildlife preserve and alert him to possible conditions that might be impacting the birds. 
Mini-Challenge #1: No IEEE award, but kudos to the students for participating! 
Project: &ldquo;A Bespoke Analysis Tool for Spatio-temporal Park Traffic Data&rdquo; 
UIC Participants: Undergraduates Dimitar Kirilov, Isabel Lindmae and Andrew Burks; graduate student Chihua Ma; and, faculty mentor Liz Marai. 
The dataset provided resident and tourist vehicle traffic in the preserve, for day trips, overnight camping, etc., as a data stream with vehicle types and entry/exit timestamps. The team developed a web-based traffic-data analysis tool that consisted of two linked heat maps showing daily activity for vehicles, as well as a histogram showing the total time spent by vehicles in the park. Combined, these views enabled the analysis of both spatial and temporal patterns in the wildlife preserve, helping the local ornithologist identify traffic patterns, correlations, or anomalies to aid his investigations. 
 
# # # # # # 
 
The IEEE VIS 2017 VAST Challenge Awards are listed on the IEEE VIS 2017 website: 
<a href="http://ieeevis.org/year/2017/info/awards/vast-challenge-awards">http://ieeevis.org/year/2017/info/awards/vast-challenge-awards</a> 
The IEEE VIS VAST Challenge 2017 website: 
<a href=http://www.vacommunity.org/VAST+Challenge+2017>http://www.vacommunity.org/VAST+Challenge+2017</a> 
Note: At IEEE VIS 2016, Marai and EVL undergraduate and graduate students won Honorable Mention in the third International Data Visualization Contest organized by the IEEE Visualization and Graphics Technical Committee&rsquo;s (VGTC) Visualization Pioneers Group (VPG). See: <a href=" https://www.evl.uic.edu/entry.php?id=2247">https://www.evl.uic.edu/entry.php?id=2247</a>

Shown here: (back row, left to right) Manu Thomas, Dimitar Kirilov, Joseph Borowicz, Bartosz Kupiec, Vijayraj Mahida and Andrew Burks; (front row, left to right) Joshua Castor, G.E. Marai and Chihua Ma. Not pictured: Isabel Lindmae and Timothy Luciani.

UIC/EVL Receives Two Honorable Mentions in IEEE VIS 2017 VAST Challenge

This paper describes a web-based traffic data analysis tool developed for the VAST 2017 Mini Challenge 1. The tool consists of two linked heat maps which allow for the inspection of daily activity for vehicles, as well as a histogram which allows for the analysis of total time spent by vehicles in the park. Combined, these views allow for the analysis of both spatial and temporal patterns in the park preserve.

Traffic data analysis tool, with a large overview interactive heat map and a detailed heat map to the side; the maps capture spatio-temporal patterns A stacked histogram allows for temporal analysis; bins can be zoomed in for closer inspection.

MC1: A Bespoke Analysis Tool for Spatio-temporal Park Traffic Data

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle. 
Index Terms - Dynamic networks, biological data visualization, rule-based modeling, protein-protein interaction networks.

A screenshot of the DIN-Viz application for analyzing the dynamics of a rule-based model of a protein-protein interaction network (i.e., a Dynamic Influence Network).

Dynamic Influence Networks for Rule-based Models

Although visualization design models exist in the literature in the form of higher-level methodological frameworks, these models do not present a clear methodological prescription for the domain characterization step. This work presents a framework and end-to-end model for requirements engineering in problem-driven visualization application design. The framework and model are based on the activity-centered design paradigm, which is an enhancement of human-centered design. The proposed activity-centered approach focuses on user tasks and activities, and allows an explicit link between the requirements engineering process with the abstraction stage-and its evaluation-of existing, higher-level visualization design models. In a departure from existing visualizationdesign models, the resulting model: assigns value to a visualization based on user activities; ranks user tasks before the user data; partitions requirements in activity-related capabilities and nonfunctional characteristics and constraints; and explicitly incorporates the user workflows into the requirements process. A further merit of this model is its explicit integration of functional specifications, a concept this work adapts from the software engineering literature, into the visualization design nested model. A quantitative evaluation using two sets of interdisciplinary projects supports the merits of the activity-centered model. The result is a practical roadmap to the domain characterization step of visualization design for problem-driven data visualization. Following this domain characterization model can help remove a number of pitfalls that have been identified multiple times in the visualization design literature. 
Index Terms - Design studies, Tasks and requirements analysis, Visualization models, Domain characterization, Activity-centered design, Functional specifications 
<iframe src="https://player.vimeo.com/video/230835822" width="640" height="360" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>
<a href="https://vimeo.com/230835822">[VIS17 Preview] Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization</a> from <a href="https://vimeo.com/vgtcommunity">VGTCommunity</a> on <a href="https://vimeo.com">Vimeo</a>.

Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization

This collaborative research project led by the colleagues at the University of Delaware aims to develop and advance tools for using image-data appearing in scientific publications, in addition to text, in order to support beneficial, targeted access to the biomedical literature. 
UIC's principal investigator, Computer Science associate professor, G. Elisabeta Marai will be responsible for the image analysis algorithms to distinguish between gel and plate images, the image analysis algorithms to distinguish between Electronic Microscopy vs Fluorescent Microscopy vs Light Microscopy and X-Rays, and the design of the visual interface to present results to end users. She will lead the design, development and implementation of the feature extraction and selection tools and techniques related to this research project.

Incorporating Image-based Features into Biomedical Document Classification

The Englewood Data Hub (EDH) is a project that brings together both crowdsourced and public data surrounding the Englewood area into a single place. 
The <a href="https://englewooddatahub.org/">EDH Resource Directory</a> makes use of the crowdsourced service and school data to display an interactive map with their locations. Its user interface (UI) allows for an exploratory experience that offers filtering of services by names and type and displays detailed information about the services available in Englewood. 
The <a href="https://englewooddatahub.org/analytics">EDH Analytics Tool</a> combines the crowdsourced service and school data with public data sets to display visualizations; specifically, the public datasets used are 2010 Census data along with Vacant Lot and Crime data from the Chicago Data Portal. 
The Englewood Data Hub has been developed by a group of researchers at the Electronic Visualization Laboratory, University of Illinois at Chicago under the direction of Prof. G. Elisabeta Marai. 
Team members include EVL research experience undergraduate students (REUs): Andrew Burks, Joshua Castor, and Isabel Lindmae. 
This project is funded by the The Joseph and Bessie Feinberg Foundation and the University of Illinois at Chicago.

Englewood Social Service Finder and Englewood Analytics

Cancer treatment courses which rely on imaging and spatially-dependent therapy involve making multiple treatment decisions (e.g., radiotherapy alone, radiotherapy plus chemotherapy, induction chemotherapy) over time. These decisions depend on complex factors, including the tumor location with respect to sensitive organs and its response to treatment, laboratory data, toxicity, anticipated side effects and survival probability. In this project, we design and develop novel statistical methodology for dynamic and personalized treatment decisions with specific application to head and neck cancer radiotherapy planning. We extend the state of the art by developing methods which take into account both imaging data features (such as radiation dosage and spatial distribution) and nonspatial data (such as demographics and toxicity) in the development of predictive models for anticipated outcomes and of the optimal treatment rules. We introduce novel similarity metrics over these hybrid, high-dimensional and sometimes incomplete patient data, and we extract informative, relevant and non-redundant features to identify similar patients. We further combine statistical models for outcome prediction with local models built over cohorts of patients. These ensemble models are used to further improve the reinforcement learning process to account for patient-specific preferences regarding side effects and survival outcomes. Specifically, we extend q-learning, a type of reinforcement learning, to account for competing, multiple outcomes and different preferences on the probability of short versus long-term outcomes. We validate our precision-driven model against known toxicity outcomes using a retrospective head-and-neck cancer dataset. 
Our specific aims are to: 
- Create quantitative methodology for extracting and selecting spatial relationships from medical images as well as non-imaging features, and generate a hybrid similarity metric which can identify similar cohorts of patients. 
- Create an ensemble model that combines global static models with dynamic local models over similar cohorts of patients with locally-sparse data. 
- Create dynamic reinforcement learning methodology which uses spatial and non-imaging features and metrics and accounts for patient preferences of multiple treatment outcomes. 
The project defines novel reinforcement learning techniques which account for multidimensional outcomes and patient preferences. Previous examples of reinforcement learning in the statistical literature have utilized examples with a small number of well-defined features, which is not applicable in the case of head and neck cancer. This project creates novel hybrid techniques for extracting informative and relevant features and similarity from imaging and nonimaging data. The project further introduces novel predictive modeling using a combination of global and dynamic local models over multiple outcomes. The proposed techniques leverage Big Data that is not only heterogeneous but also high-dimensional and locally sparse, in order to derive a novel precision-medicine approach. 
The empirically-derived treatment rules developed in this project have the potential to improve the standard of care (i.e., treatment plans chosen by the tumor treatment board) and the quality of life of surviving patients. The methods developed in the proposal may be used to derive optimal treatment strategies across not only a variety of spatially-dependent cancer diagnoses, but also other chronic conditions including mental health disorders, substance abuse diseases, or diabetes, that require making multiple decisions that must weigh the tradeoffs between efficacy and toxicity.

QuBBD: Precision E-Radiomics for Dynamic Big Head & Neck Cancer Data

Head and neck squamous carcinomas affect all ethnicities and age groups, accounting for significant mortality and therapy-related
side effects. Over 50,000 new cases are diagnosed each year in the United States, leading to large, rich repositories of patient data. For each of these cases, oncologists need to anticipate survival, oncologic, and side effect outcomes associated with treatment strategies in order to select a treatment which balances efficacy and toxicity. However, despite the wealth of data available, risk prediction algorithms for cancers are rudimentary and incorporate only a handful of patient characteristics, largely due to a lack of computational methodologies. 
This project will develop a computational methodology that supports the construction of a validated precision model of patient-specific outcomes for head and neck cancers, based on demographics, toxicity, and complex imaging data. The methodology will be the first in the field to include both complex imaging and non-imaging data, while taking into account large-scale biological and clinical correlates. A web-based environment will further allow practitioners to interactively explore and communicate the model mechanics and outcomes to stakeholders. The approach is innovative through its leverage of big imaging data repositories and through its unique blend of computational modeling principles from bioengineering, statistics, and computer science (medical imaging and visualization). 
From a clinical perspective, this integrative approach is novel in the field of cancer therapy. The resulting model and web-based
environment will mark a significant advance in biomedical computing because it will be able to identify, for the first time, specific subgroups who are at risk for distinct oncologic, toxicity, and survival profiles. Furthermore, the currently clinical practice paradigm for head and neck treatment is stage-driven. With validation, this novel approach can identify patients based not on clinical staging, but on precision modeling using cohort data and similar sets of patients. The methodology is applicable to clinical practice involving human subjects and has the potential to change the standard of care and outcomes of treatment in the field.

SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment

The <a href="http://sage2.sagecommons.org/2017/11/27/sage2-sc17-demonstrations-birds-of-a-feather-and-scinet-participation/">SC17 SAGE2&trade; BOF presentation material</a> is now posted online. 
* * * * * * * * * * * 
SAGE2 @ SC17: Demonstrations, Birds of a Feather, and SCinet Participation 
NOVEMBER 12-16, 2017 - Members of the SAGE2&trade; Team who attended the IEEE/ACM SC17 conference - from University of Hawaiʻi at Manoa&rsquo;s Laboratory for Advanced Visualization &amp; Applications (LAVA), University of Illinois at Chicago&rsquo;s Electronic Visualization Laboratory (EVL), and University of Hawaiz&rsquo;i at Hilo - were involved in many tech transfer activities, notably research demonstrations, a Birds of a Feather (BOF) meeting, and developing visual analytics of network performance for the SCinet DevOps team. The SC17 conference was held at the Colorado Convention Center in Denver. 
SAGE2 BOF 
The &ldquo;SAGE2 9th Annual International SC BOF: Scalable Amplified Group Environment for Global Collaboration&rdquo; meeting was held on Thursday, November 16. The BOF provided a Forum for SAGE2 developers to meet with the the SAGE/SAGE2 global user community and potential users; specifically: 
<ul><ul><ul>
<li>To introduce key SAGE2 team members
<li>To highlight new SAGE2 sites and applications
<li>To provide an overview of the 2017 SAGE2 user survey and recommended roadmap
<li>To introduce SAGE2 v3 (named &ldquo;Waipi&rsquo;o&rdquo;)
<li>To solicit feedback
</ul></ul></ul> 
SAGE2 presentation materials can be downloaded or accessed as follows: 
<ul><ul><ul>
<li>SAGE2 Presentation in Powerpoint with Embedded Movies (432MB) <a href="http://sage2.sagecommons.org/download/2332/">SAGE2 BOF2017 PPT</a></li>
<li>SAGE2 Presentation PDF (2MB) <a href="http://sage2.sagecommons.org/download/2329/">SAGE2 BOF2017 PDF</a></li>
</ul></ul></ul> 
SAGE2 Videos: 
<ul><ul><ul>
<li><a href="https://www.youtube.com/embed/q6AdaeoFDm8">SAGE2 Cloud</a></li>
<li><a href="https://www.youtube.com/embed/uU5HZtmvS10">SAGE2 Image Geolocation</a></li>
<li><a href="https://www.youtube.com/embed/l2pRLhw6GSE">SAGE2 and JupyterLab</a></li>
<li><a href="https://www.youtube.com/embed/6E7pdyuhwhA">SAGE2 Remote PointersZ</a></li>
<li><a href="https://www.youtube.com/embed/9atA_h_t5pM">SAGE2 Speech Commands</a></li>
<li><a href="https://www.youtube.com/embed/jglsZNQ8T-g">SAGE2 SageRDI: SAGE River Disaster Information</a></li>
<li><a href="https://www.youtube.com/embed/ft11wOcUjvA">SAGE2 Synchronized Movie Playback</a></li>
</ul></ul></ul> 
SAGE2 Demonstrations 
SAGE2 demonstrations took place in the Center for Data Intensive Science/Open Commons Consortium research booth on the exhibition floor, November 13-16, 2017. Booth attendees saw firsthand SAGE2 v3&rsquo;s many new and advanced features, and talked with the key developers: 
<ul><ul><ul>
<li>SAGE2Cloud: create and manage SAGE2 server instances in Docker containers</li>
<li>JupyterLab plugin: link your science workflows with SAGE2 using the next version of Jupyter</li>
<li>Performance monitoring: monitor performance of your server and display clients</li>
<li>A user management system</li>
<li>Unity3D apps (WebGL export)</li>
<li>Remote collaboration features: remote view, remote pointers</li>
<li>Voice commands</li>
<li>Better webviews and user interface</li>
</ul></ul></ul> 
SAGE2 SCinet Participation 
SCinet is the SC conference&rsquo;s highly sophisticated, high-performance network infrastructure that is designed and implemented by a large group of academic, government and industry volunteers. SCinet&rsquo;s DevOps Team runs the internal software infrastructure for SCinet - including software to monitor and measure network flows to ensure services and provide alerts when there are issues. Starting last year, SCinet organizers used SAGE2 on a tiled display wall outside their Network Operations Center in order to provide attendees with large-scale images about the network. This year, SCinet continued to use SAGE2, but also wanted help producing visualizations of network flows to gain more insight into usage. Andrew Burks, a member of the SAGE2 Team from EVL, produced several visual analytic diagrams over the course of the week, which were displayed both on SCinet&rsquo;s wall as well as in the SAGE2 research booth. 
SAGE2 receives major funding from the National Science Foundation (award #ACI-1441963). 
SAGE and SAGE2 are trademarks of the University of Illinois Board of Trustees. 
For more information, see: <a href="http://www.sagecommons.org">www.sagecommons.org</a>

SAGE2 BOF2017 PPT

SC17 SAGE2 BOF Presentation

SC17 SAGE2 BOF materials now posted online

We introduce a web-based visual comparison approach for the systematic exploration of dynamic activation networks across biological datasets. Understanding the dynamics of such networks in he context of demographic factors like age is a fundamental problem in computational systems biology and neuroscience. We design visual encodings for the dynamic and community characteristics of these temporal networks. Our multi-scale approach blends nested mosaic matrices that capture temporal characteristics of the data, spatial views of the network data, Kiviat diagrams and mirror glyphs that detail the temporal behavior and community assignment of specific nodes. A top design specifically targeted at pairwise visual comparison further supports the comparative analysis of multiple dataset activations. We demonstrate the effectiveness of this approach through a case study on mouse brain network data. Domain expert feedback indicates this approach can help identify trends and anomalies in the data. &copy; 2017 Society for Imaging Science and Technology. 
[DOI: 10.2352/J.ImagingSci.Technol.2017.61.6.000000]

Integration of temporal community characteristics into the brain slice of an aged mouse, across 64 time steps.

RemBrain: Exploring Dynamic Biospatial Networks with MosaicMatrices and Mirror Glyphs

Committee: 
G. Elisabeta Marai (Chair) 
Robert Kenyon 
Andrew Johnson 
Angus Forbes (University of California, Santa Cruz) 
Tamara Munzner (University of British Columbia) 
Date & Time: Feb 5th, 2018, 3:30 pm - 5:30 pm 
Location: 2036 ERF (Electronic Visualization Laboratory CAVE2) 
Abstract: 
Recent neuroscience research indicates that cognitive operations are performed not by individual brain regions working in isolation, but by networks that consist of several discrete brain regions. These discrete brain regions act in synchrony and share functional connectivity. The study of brain networks contains both non-spatial data - dynamic functional connections within the brain - and spatial data - 2D/3D medical scans of the brain. To further understand the relationships between dynamic functional connectivities and physical structures of the brain, researchers analyze dynamic brain networks over multiple experiments or with different models. Similarly, bioinformatics research also contains problems about dynamic biological networks that require the integration of spatial and nonspatial data and multi-run comparison. Due to the complexity of biological data that can be collected, modeled and analyzed thanks to technological and algorithmic advancements, the difficulty in studying biological networks has increased over the last decades. Visualization provides an efficient way to help biologists understand, communicate, and gain insight into their biological data through visual exploration and analysis. 
In this dissertation, I propose a set of visual approaches for the analysis of ensemble dynamic biological networks. This set of approaches took shape through the development of multiple prototype visual analytics tools which were aimed to solve complex problems in multiple biological subdomains. The steering of the approach was supported through an in-depth understanding of both the encompassing biological domains and problem space, as well through a survey of existing visual approaches helping to generalize the paradigms used when confronting these problems and the complexity of their data.

Doctoral Defense Announcement: &ldquo;Visual Analysis Techniques for Dynamic Biological Networks&rdquo;

Drug-drug interaction (DDI) alerts safeguard patient safety during medication prescribing, but are often ignored by physicians. Despite attempts to improve the usability of such alerts, physicians still mistrust the relevance of simplistic computerized warnings to support complex medical decisions. By building on prior fieldwork, this paper evaluates novel designs of trust - eliciting cues in DDI alerts. A sequential mixed-method study with 70 physicians examined what trust cues improve compliance, promote reflection, and trigger appropriate actions. In a survey, 52 physicians rated the likelihood of compliance and usefulness of redesigned alerts. Based on these findings, alerts were assessed in a scenario-based simulation with 18 physicians prescribing medications in 6 patient scenarios. Our results show that alerts embodying expert endorsement, awareness of prior actions, and peer advice were less likely to be overridden than current alerts, and promoted reflection, monitoring, or order modifications - thus building towards greater attention to patient safety. 
Keywords: healthcare information systems; health informatics; clinical alerts; trust in socio-technical systems; interaction design; decision-making 
<ahref="https://doi.org/10.1093/iwc/iwx020">doi:10.1093/iwc/iwx020</a>

DDI alerts were displayed in an experimental setting that simulated an inpatient meeting room where physicians use a workstation to prescribe medications. Interruptions were simulated using a second computer and screen interactions recorded using Camtasia

Design and Evaluation of Trust - Eliciting Cues in Drug - Drug Interaction Alerts

Recent neuroscience research indicates that cognitive operations are performed not by individual brain regions working in isolation, but by networks that consist of several discrete brain regions. These discrete brain regions act in synchrony and share functional connectivity. The study of brain networks contains both non-spatial data dynamic functional connections within the brain and spatial data 2D/3D medical scans of the brain. To further understand the relationships between dynamic functional connectivities and physical structures of the brain, researchers analyze dynamic brain networks over multiple experiments or with different models. Similarly, bioinformatics research also contains problems about dynamic biological networks that require the integration of spatial and nonspatial data and multi-run comparison. Due to the complexity of biological data that can be collected, modeled and analyzed thanks to technological and algorithmic advancements, the difficulty in studying biological networks has increased over the last decades. Visualization provides an efficient way to help biologists understand, communicate, and gain insight into their biological data through visual exploration and analysis. 
In this dissertation, I propose a set of visual approaches for the analysis of ensemble dynamic biological networks. This set of approaches took shape through the development of multiple prototype visual analytics tools which were aimed to solve complex problems in multiple biological subdomains. The steering of the approach was supported through an in-depth understanding of both the encompassing biological domains and problem space, as well through a survey of existing visual approaches helping to generalize the paradigms used when confronting these problems and the complexity of their data.

RemBrain 2.0 implements a summary+detail visual approach for the analysis for comparative analysis of multiple brain networks at four scales: groups of subjects, individual subjects, activations of a subject, and ROIs of an activation.

Visual Analysis Techniques for Dynamic Biological Networks

We present the design and evaluation of an integrated problem solving environment for cancer therapy analysis. The environment intertwines a statistical martingale model and a K Nearest Neighbor approach with visual encodings, including novel interactive nomograms, in order to compute and explain a patient&rsquo;s probability of survival as a function of similar patient results. A coordinated views paradigm enables exploration of the multivariate, heterogeneous and few-valued data from a large head and neck cancer repository. A visual scaffolding approach further enables users to build from familiar representations to unfamiliar ones. Evaluation with domain experts show how this visualization approach and set of streamlined workflows enable the systematic and precise analysis of a patient prognosis in the context of cohorts of similar patients. We describe the design lessons learned from this successful, multi-site remote collaboration. 
Index Terms - Visual analytics, precision medicine, design studies, nomograms, parallel coordinate plots, activity-centered design. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190,&ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo; 
National Science Foundation: 
NSF-CNS-1625941, &ldquo;MRI - Development of Continuum: A Virtualized Attentive Environment for Amplified Collaboration&rdquo; 
NSF-DMS-1557559, &ldquo;QuBBD Collaborative Research SMART Spatial Nonspatial Multidimensional Adaptive Radiotherapy Treatment&rdquo;

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