|Due October 9||CS 7450 - Information Visualization||Fall 2013|
This assignment will familiarize you with a number of systems that have been built for analyzing multivariate data sets. You will be working with InfoZoom, Spotfire, and Tableau.
The goals of the assignment are for you to learn the capabilities provided by these types of systems, learn the visualization methods that they provide, and assess their utility in analyzing information repositories. You will work with some provided data sets in the assignment. Think about the kinds of questions that an analyst would be asking about the data sets. IMPORTANT: For the assignment, you only need work with two of the three commercial systems. The choice of which two is up to you. (Feel free to work with all three as well.)
The assignment has four parts:
1. Gain familiarity with the systems
Familiarize yourself with the visualization techniques and the user interfaces of the different systems. Each one has a tutorial that you should try out with a sample data set. Work your way through the tutorial and become familiar with the system, its interface and its capabilities.
2. Examine the sample data sets
Each tool includes a few sample data sets, but often it's best to learn with something new. Five data sets are supplied the Resources page of t-square for you to consider: foods' nutritional data (5976 items, 32 vars.), stocks (500 items, 30 vars.), baseball statistics (322 items, 24 vars.), college information (51 items, 22 vars.) and professor's salaries (1160 items, 16 vars.). You must work with the food nutrition data set and you are free to pick the one other set that is most interesting to you. Briefly scan the text of the files and familiarize yourself with the variables. Generate and write down (you will need to turn them in) a few hypotheses to be considered, tasks to be performed, or questions to be asked about the data elements. Think about all the different kinds of analysis tasks that a person might want to perform in working with data sets such as these. For instance, someone working with a data set about breakfast cereals might have tasks like:
3. Load and examine the data sets into the systems
Load the nutrition and other data set that you selected into each of the two visualization tools you selected, then consider your hypotheses, tasks, and questions. Also use the systems to explore the data sets and see if you can discover other interesting or unexpected findings in the data sets. Put yourself in the shoes of a data analyst, and consider questions that such a person would confront.
4. Write a report on your findings
Write up a summary of your exploration process, findings, and impressions of the systems. Include your hypotheses/tasks/questions and what you found. Furthermore, critique the different tools in a general sense. (Feel free to include screenshots to help explain your analyses and critiques.) What are the systems' strengths and weaknesses? How do their visualization capabilities differ? For what kinds of user tasks is each tool suited? Focus more here on the visualization techniques as opposed to the particular user interface quirks, though you should feel free to comment on UI aspects when they are particularly good or bad. Additionally, for each tool, list one unexpected finding, insight, or discovery made while exploring one of the datasets with that tool. Explain how the system helped to facilitate the finding.
We recommend that you not walk through each question/task one-by-one for each of the two systems you used. (There simply won't be space to do so.) You might want to include specific examples of how the systems assisted or did not assist work on specific tasks, however. Point out interesting, insightful observations; you don't need to tell us how a system works -- we already know that. Think of this like a report to your manager who wants to know what each system can provide, its pros and cons. Focus specifically on how its visualizations help or hinder analysis. How dod the systems compare?
Your document is limited to a maximum of 8 pages, single-spaced, reasonable font size, including embedded screenshots. Please bring two hardcopies to class on the day that it is due.
Acknowledgments: Special thanks go out to Chris Ahlberg of Spotfire, Jock Mackinlay and Chris Stolte of Tableau, Michael Spenke and Christian Beilken of the Fraunhofer Institute for Applied Information Technology, and Steven Pesklo and Doug Molumby of Softlake Solutions for all their help in getting and working with the systems.