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ECE695D

Note that this is an archived version for the 2011 edition of the course! See this page for the current (2013) edition!

Announcements

  • Tuesday, November 20: Thanks all for a great semester and I hope you will all continue your work in visual analytics in the future!
  • Monday, November 14: We have scheduled two separate beta demo times: Thursday 11am-12pm, and Friday 1pm-2pm in POTR 127. Please arrange with me by e-mail or in class to find a 10-minute slot for you to present the work to the instructors (preferably during one of these two times).
  • Monday, November 8: I have changed the beta release due date to Monday, November 14 at 11.59pm (instead of Saturday, November 12).
  • Thursday, November 3: Hans Rosling was mentioned in class today; for those of you who have not seen any of his TED talks, here is his most famous video from 2006.
  • Monday, October 31: Happy Halloween! Tomorrow you will be given the midterm take-home exam and you will have exactly one week to return it. Any source is allowed (please cite them, even if it is only Wikipedia), but you are not allowed to collaborate and help each other!
  • Wednesday, October 12: The lecture tomorrow (Thursday, October 13) will be given by Dr. Yun Jang (of the PURPL laboratory).
  • Thursday, September 22: Remember that literature reviews are due by midnight tonight!
  • Monday, September 12: We have received all proposals now and are reviewing them. You will receive initial feedback as soon as possible. Remember to prepare 3-minute presentations of your proposal for class on Thursday, September 15!
  • Friday, September 2: The Jigsaw distribution and the Atlantic Storm dataset in .jig format has been made available on the Blackboard site. Let me know if you do not have access to the Blackboard and still want access to the system.
  • Monday, August 29: The assignments have all been updated in the Blackboard system. It should now be possible to submit your analytical exercises. Also, those of you who encountered trouble submitting your paper critiques last week, please e-mail them directly to Prof. Elmqvist. For the future, be careful about non-ASCII characters when submitting comments to the Wiki (and don't be afraid to edit if need be).
  • Thursday, August 25: Remember that the first paper critique is due on Friday (tomorrow), and the first phase of the analytical exercise is due on Thursday, September 1! (Edit: This incorrectly said "September 8"; September 1 is the correct date.)
  • Friday, August 19: The first lecture will be given on Tuesday, August 23. Meanwhile, this website will slowly be updated with information. See you on Tuesday!

ECE 695D - Introduction to Visual Analytics

  • Course hours: TTh 9:00am-10:15am in EE 224
  • Term: Fall 2011
  • Instructors:
    • Niklas Elmqvist, Assistant Professor of Electrical and Computer Engineering
      • E-mail: elm@purdue.edu
      • Office: MSEE 270 (second floor of the MSEE building)
      • Office hours: Tuesdays, 10:30am-11:30am (or by appointment)
    • David Ebert, Silicon Valley Professor of Electrical and Computer Engineering
      • E-mail: ebertd@purdue.edu
      • Office: MSEE 274 (second floor of the MSEE building) and POTR 228
      • Office hours: by appointment only
  • Textbook: No official textbook, research papers (see the ReadingList).
  • Prerequisites: Introductory knowledge of one or more of the following areas: data analysis, knowledge management, statistics, computer graphics, visualization.
  • Course Schedule: See the DetailedSchedule.
  • Assignments:

Note: This website serves as the formal syllabus for ECE 695D. It is subject to change as the course progresses, so please keep coming back to find more information!

Introduction

The development of terascale and petascale computing systems and powerful new scientific instruments has created an unprecedented growth of scientific data. Unfortunately, this sea of data has not had the transformational effect that was expected on science and engineering, but has instead become a bottleneck in itself. Fundamental new techniques are needed to turn this data deluge into the useful, understandable information and knowledge necessary for transformational science and engineering, creating interactive knowledge environments for exploration, design, and discovery.

Visual analytics---the science of combining interactive visual interfaces with automatic algorithms to support analytical reasoning and build synergies between humans and computers---is an emerging science that has been created to address this challenge. Originally launched in 2004 by the U.S. Department of Homeland Security to enable harnessing the data deluge in tackling the overarching economical, environmental, and security-related challenges facing society, the discipline now has funded efforts from the National Science Foundation and the U.S. Department of Energy, as well as several international efforts of similar scale and magnitude.

Visual analytics is used in all areas spanning science, engineering, business and government; examples of specific application areas include intelligence analysis for homeland security, business intelligence support to help businesses acquire a better understanding of their commercial context, mobile graphics for emergency first responders, network log analysis for computer security, and health monitoring for disease outbreak prediction and response.

This course will serve as an introduction to the science and technology of visual analytics. The course contents will include both theoretical foundations of this interdisciplinary science as well as practical applications of integrated visual analysis techniques on real-world problems.

Overview

This course will cover topics in visual analytics. The format for the course will be group discussions of papers, lectures by the instructors, and student presentations of papers. There will also be a take-home midterm exam. The grading will be based on participation in class, critical assignments, and class projects. Class projects may be done individually or in groups. Projects have the potential of leading to work that forms the basis of an undergraduate research projects, Master's thesis, or Ph.D. research topic.

Schedule

See the DetailedSchedule for more information.

Readings

There is no official textbook for this course. Students will read and discuss seminal and current technical research papers. A list of readings (in progress and subject to frequent update) is available on the ReadingList.

The following books may be useful as references.

  • Illuminating the Path: The R&D Agenda for Visual Analytics, Editors: James J. Thomas and Kristin A. Cook (online version)
  • The Visualization Handbook, by Charles Hansen and Christopher Johnson, Academic Press, 2005
  • The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, by William Schroeder, Ken Martin, Bill Lorensen, 2nd Edition, 1997, (ISBN 0-13-954694-4).
  • Readings in Information Visualization: Using Vision to Think, by Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman, Morgan Kaufmann

Assessment Methods

The course outcomes will be assessed through student demonstration of a completed visual analytics project, submission of working program(s), oral and written presentation of results (literature survey, alpha release report, beta release report, regular meetings of project teams with the instructor, and the final project report), and a take-home midterm exam. The overall knowledge acquisition of visual analytics will be assessed by student oral presentations of papers, through the completion of a literature review, and through several initial project assignments.

Grades

Grades will be assigned on the following grounds:

  • Paper reading and evaluation - 15%
  • Analytical exercise - 15%
  • Paper presentations and class participation - 10%
  • Midterm exam - 15%
  • Class project - 45%

Phases may be turned in up to one week after the due date with a 30% grade penalty. Phases will not be accepted more than a week late. Plus/minus grading will be used in this class.

Assignments

This course has five types of assignments: an introductory visualization assignment, weekly paper critiques, paper presentations, and the course project. More details on these are given below:

  • Analytical Exercise: To get a firsthand experience of some of the problems that our potential end users face, you will perform an analytical reasoning exercise to find a hidden threat in collection of police and intelligence reports. The exercise will not involve any programming and can be done entirely by hand on paper.
    • Released: Thursday, Aug 25 (Week 1)
    • Deadline (Phase 1): Thurday, Sep 1 (Week 2)
    • Deadline (Phase 2): Thurday, Sep 8 (Week 3)
  • Paper Critiques: Each student taking this course for credit must critique a research paper every week from the ReadingList for that week.
    • Deadline: every Friday (with some exceptions)
  • Paper Presentations: Students must present three (3) research papers in-class during the course. Presentations should be 10-15 minutes.
  • Course Project:
    • Released: Thursday, Aug 25 (Week 1)
    • Deadlines: several milestones
      • Thursday, Sep 8 (Week 3) - Project Proposal
      • Thursday, Sep 22 (Week 5) - Literature Review
      • Thursday, Oct 13 (Week 8) - Alpha Release
      • Thursday, Nov 10 (Week 12) - Beta Release
      • Sunday, Nov 20 (Week 13) - Paper Draft
      • Sunday, Nov 27 (Week 14) - Final Paper
      • Sunday, Dec 4 (Week 15) - Final Release
      • Sunday, Dec 4 (Week 15) - Final Reviews
      • Last week of classes (Week 16) - Presentations

There is no final exam in this course. There is, however, a take-home midterm exam that will be administered halfway through the course.

Academic Integrity

Students must conform to Purdue's policy on academic integrity. More specifically, Purdue prohibits "dishonesty in connection with any University activity. Cheating, plagiarism, or knowingly furnishing false information to the University are examples of dishonesty." [University Regulations, Part 5, Section III, B, 2, a]

Please see the detailed page on AcademicIntegrity. Note that you are responsible for knowing this information---ignorance is not a valid excuse.

Campus Emergencies

In the event of a major campus emergency, course requirements, deadlines and grading percentages are subject to changes that may be necessitated by a revised semester calendar or other circumstances. Information about changes in this course can be received from the course website, or the Blackboard site, or by contacting the instructor by email (elm@purdue.edu) or office phone (765-494-0364).

Please see the detailed page on CampusEmergencies for more information.

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Page last modified on August 18, 2013, at 07:39 PM