Project 2

Monster Mash

Project due Wednesday 10/17 at 9 pm Chicago time

Project 2 will be the first group project and will focus on converting data into a form for visualization using processing on the touchscreen wall in the classroom.

Group size will be 3 people per group. You can chose who you want to be in a group with, but you will be working with different people in each project. I will create groups for people that do not form groups on their own by Friday.

You should very quickly set up a web page for your group project and send the URL to andy along with the names of the members of your group. The final webpage for the project will be public; the in-process web pages do not need to be public as long as Andy and Arthur have access. Each Friday of the project each team member should post on the project web site an overview of what he/she did on the project that week. This comes in handy when assigning ratings to your collaborators and making sure that everyone is contributing.

Note that the due date for project 2 is on a Wednesday, rather than the normal Monday. The classroom (and its wall) will be reconfigured as demo space for a conference the week of 10/9 and 10/11, and will not be available for testing.



In this project we are going to investigate the popularity of different kinds of monsters over the years in movies.

We will make use of the internet movie database (www.imdb.com). The raw data is available at http://www.imdb.com/interfaces#plain

The keywords that people have applied to various films will let you see which films involve vampires, werewolves, aliens, robots, giant monsters, witches, zombies (voodoo kind), zombies (ghoul kind), Dr. Frankenstein's monster, sea monster / lake monster, giant insects, mutants, demons, monster plants, Bigfoot / Sasquatch / yeti, ghosts, and the various subcategories of each. You are encouraged to add other categories here, and you should look at the variations in each of the keywords to make sure you are getting the most accurate data.

The application should help the user investigate various things, e.g.


Much of your work will involve the keyword information but you will also need other information such as genres, rating, business, country of origin, and certificates.

We will not include any video games (VG) or TV episodes or any films that have 'Adult' or 'Documentary' as a genre, but TV movies (TV) and direct to video releases (V) should be included in the visualization.

You should first download a copy of the various data files and take a look at them to try and understand their structure. Then you need to decide how you want to integrate these various files together into a form that you can visualize. This may involve writing shell scripts or programs to process the files, or loading the information into a database you can access via processing. Note that there are a lot of TV episodes in the IMDB so I would suggest filtering those out of your database / files ASAP. If you do choose to use a database make sure that it will work on the computer driving the classroom wall.

We will deal with films from 1890 through 2012

You should convert the budgets (available from the business data) to 2012 dollars by taking inflation, and currency conversion if necessary, into account so they can be compared more accurately. This will allow you to cluster the films into big budget, low budget, and no budget films, based on a criteria that you create.

Data may not be available in all categories so you need an effective way of showing which information is missing. Missing data does not mean that you toss out the film; it means you find a way to show that particular pieces of information are missing (e.g. a film has an unknown genre or an unknown budget)

The current US MPAA rating system (G, PG, PG-13, R, NC-17) began in 1968 and has changed a few times since then (e.g. PG was originally M and then became GP before becoming PG, and NC-17 was formerly X). Some earlier films have been re-rated, but many are not rated, though they would be equivalent to G or PG. Wikipedia has a nice article with all the details.

There will be two visualizations

The main visualization will be a timeline. The timeline should be dynamic so the user can see all the data at once, or a subset of years in more detail, or cluster the data by decade. For each year the user should be able to see the total number of monster films, and how that compares to the total number of films for that year, or look at details for just the monster films that year. The data for each year can further be broken down and visualized by categories.

You should come up with a good color coding scheme allowing the user to color the timeline graph by:

You should come up with an intuitive interface to let people filter the data by the above categories, and combinations of those categories e.g. I want to see only high budget vampire musicals from 1930 to 1960, or I want to see what genres are the most common in the 1970s for ghost stories, or I want to look at all the monster movies but colour code them into high-budget, low-budget, and no-budget films, or I want to compare the number of ghost stories to giant monsters over all the decades, allowing the user to show all the data or subsets of the data.

The user should be able to select their options from menus of choices

The user should be able to select a year or range of years and see a tabular version of the data in the timeline. The user should also be able to bring up data on the individual films.

Note that a single film may have multiple monster keywords and/or multiple genres so you will need to decide, and defend your decision, how you will integrate that data with a film that has a single monster keyword and genre. Note that picking one keyword or one genre from the set is not a valid solution.


Aside from the timeline, another visualization will show the most common genres, countries of origin, budget level, certificates, quality level, and other keywords for a given monster or combination of monsters.

There are various data processing steps necessary to get the data ready for visualization. You can do this processing in your main application, or with a separate application, or through scripts or database commands. You need to document this process, and to as great an extent as possible automate this process, so that it would be easy for a person to take the current version of the IMDB data and update the data that your program makes use of.

Your program should be interactive and respond quickly when the user changes the data he/she is viewing. This means you may need to create a set of pre-processed data files that are designed to be visualized quickly, rather than running complex queries or matching algorithms each time the user touches a button. These are also things that should be documented.



For a C you need ...


For a B you need to add ...


For an A you need to add ...




You should create a set of web pages that describe your work on the project. This should include:
all of which should have plenty of screenshots with meaningful captions. Web pages like this can be very helpful later on in helping you build up a portfolio of your work when you start looking for a job so please put some effort into it.

Be sure to document any external libraries or tools that you make use of - give credit where credit is due.

You should also create a 2-3 minute YouTube video showing the use of your application including narration with decent audio quality. That video should be in a very obvious place on your main project web page. The easiest way to do this is to use a screen-capture tool while interacting with a scaled-down version of the application, though you will most likely find its useful to do some editing afterwards to tighten the video up. Its also a good idea to have a video like this available as a backup during your presentation just in case of gremlins.
You may want to shoot this video on the wall itself.

The web page including screen snapshots and video need to be done by the deadline so be sure to leave enough time to get that work done.

I will be linking your web page to the course notes so please send me a nice 1280 x 361 jpg image of your visualization for the web. This should be named p2.<someone_in_your_groups_last_name>.jpg. 

When the project is done, each person in the group should also send me a private email with no one else cc'd ranking your coworkers on the project on a scale from 1 (low) to 5 (high) in terms of how good a coworker they were on the project. If you never want to work with them again, give them a 1. If this person would be a first choice for a partner on a future project then give them a 5. If they did what was expected but nothing particularly good or bad then give them a 3. By default your score should be 3 unless you have a particular reason to increase or decrease the number. Please confine your responses to 1, 2, 3, 4, 5 and no 1/3ds or .5s please. I will average out all these scores for projects 2 through 4 and keep them in mind when assigning final grades to projects 2 through 4.

Each group will show their visualization to the class on the wall and describe its features. This allows everyone to see a variety of solutions to the problem, and a variety of implementations. Rehearse your presentation ... several times. All team members are expected to participate equally in that presentation. The length of the presentations will be 5 minutes. During each talk each group in the audience should write one question for the speaking group, and hand it to them at the end of their presentation. The speaking group should add a page to their website by Friday 10/19 giving the questions (and the group who asked it) and an answer to the question.



last revision 10/12/12 -integrated the notes on data processing fromt he email I sent out