CS 795/895 - Information Visualization
Fall 2012: Tues/Thurs 3-4:15pm, E&CS 2120

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Semester Project

Report Due: Fri, Dec 7, 2012 by 11:59pm
Presentation/Demo: Sat, Dec 8, 2012 during exam time

Added link to contest webpage (with full dataset) 10/9 -MCW


The US Air Force is interested in detecting emerging technology trends. One topic they are interested in is quantum sensing (see Quantum Sensing at Sandia National Labs for an overview).

You will be provided with a dataset of publications resulting from searches on key "Terms of Art" in quantum sensing. The citations will provide a minimum initial set of data. You are encouraged to gather more data through further search of publication full texts, web-based searches (Google trends, Google public data), etc.

The task is to produce an innovative visualization of the emergence, growth, and potential future of the field of quantum sensing. The visualization may be a static infographic or an interactive visualization.

This project also can qualify as an entry in a campus-wide contest open to all ODU students. You are encouraged to create a team and enter your project in the contest, which runs through December 14, 2012. The winning entry will provide the best experience for a decision maker to evaluate his/her strategic level of investment - in time, attention, or resources - in Quantum Sensing technology.


Details on the full database will be made available next week. (See Infographics Contest page for full data set)

See "Preliminary Quantum Sensing Database" under Modules on our Blackboard page. This file contains publication counts related to quantum sensing for various categories. The Excel file has columns for total publications and publications per year (1998-2008) in each of the following categories:

  • Country by Year
  • Organization by Year
  • Author by Year
  • Descriptors by Year
  • Document Type by Year

In the section labeled “Descriptors by Year” you will find a long list of ‘Terms of the Art’ in all-things-quantum. This data can be filtered on quantum sens –ing, –or, –ors, etc., (and any terms of art related to QS, such as Quantum dots, etc.) to provide a year-to-year record of published paper ‘events’ (that is, a full text publication with the term occurring in it at least once = one ‘event’) in QS. The “Descriptors by Year” data can be a start, as nearly every event should (!) appear in the official competition database.

In comparison to the “Descriptors by Year”, the official competition database will include:

  • each ‘event’ based on QS terms of the art (NOT on all-things-quantum terms of the art)
  • the ‘event’ publication citation information (though NOT the publication full text)
  • search results from the year (about) 1990.

Adventurous teams will want to, perhaps, consider looking at trends in QS-related researchers, lab sites, investments, patents, conferences, etc. To compete well in the competition, students should build teams, e.g., including full-text data miners, to propel their best competition entry.


Students should work on a project in teams of 1-2.

For the purposes of the contest, you may also work with students outside of this class. If this is done, you must provide an accounting of what parts of the project you personally contributed and which parts were contributed by outside students.

Important Milestones

  • Oct 11 - Initial project description. One-page document listing project members and initial approach.
  • Nov 1 - Project mid-way progress report due. This should be an analysis of the problem along with a detailed design for the system. Include a mock-up (sketch, Visio, PPT) of your visualization approach. Describe the development tools you will use. Describe additional data that you have gathered or plan to gather. (Suggested length: 3-5 pages, no required format)
  • Dec 7 - Report describing the system due
  • Dec 8 (during exam time) - Project presentations and demos.

You are welcome to meet with me as needed to discuss your progress and any issues that arise.


  • 15-20 minutes
  • The presentation should include an overview of any data cleaning required, a description of your system, future work, problems/things you learned.
  • The main part of the presentation is a live demo of the system.


  • Formatted according to guidelines for Transactions on Visualization and Computer Graphics (same format as InfoVis papers). Guidelines and templates are available at http://www.cs.sfu.ca/~vis/Tasks/camera_tvcg.html.
  • You are encouraged to use a teaser image to provide a picture of your system on page 1.
  • Required heading elements: Title, Author Names and Affiliations (dept, university), Abstract. Index Terms are not required.
  • Min: 5 pages, max: 10 pages
  • Treat this as you would a conference paper - you've read enough of them to know the general outline. You must include sections for Introduction, Related Work, Conclusions, and References, along with others as appropriate.
  • Remember that all figures and tables must include appropriate captions (and should be designed according to the principles we've discussed in class).
  • After the Conclusions section, add a section titled 'Final Thoughts' describing your experience working on the project. What were problems you faced? What things did you learn?
  • This paper must be in your own words. Especially when describing related work, you must resist the urge to copy (either directly or indirectly) from the paper you are referring to.
  • Submission: One member of the group should email a PDF version of the report to me with the subject line CS 795/895 Final Report

See Process and Pitfalls in Writing Information Visualization Research Papers by Tamara Munzner


I will evaluate the overall quality of your project, including all milestones and components. The following questions will be important during that evaluation process:

  • Does the system work, ie, does it read in the data and present an effective visualization of the data?
  • Is the visualization an effective representation of the data?
  • Does the visualization support different analytical questions about the data?
  • Is the visualization creative and does it illustrate some new ideas? (This isn't absolutely crucial, but simply re-implementing a well-known tool or technique is not so appealing.)
  • Was your presentation an effective discussion and portrayal of the project?
  • Does your report help someone understand the problem and how your system addresses that problem?