1. LECTURE 01: INTRODUCTION TO VISUAL ANALYTICS January 14, 2015 COMP 150-04 Topics in Visual Analytics

2. COMP 150-12: Topics in Visual Analytics Fall 2014 MW 6:00-7:15pm Lecture: Halligan 111A Lab: Halligan 118

3. People Instructor: R. Jordan Crouser jordan.crouser@gmail.com Office Hours: W 7:15-8:15p Location: TBD Teaching Assistant: TBD

4. People 3 Minute Biographies: Your (preferred) name Your major / area of focus Year (grad vs. undergrad) - If grad, where did you do your undergrad? Technical background - Programming language(s) you know/like - Any experience in web design, visualization, HCI 2 Questions: What do you hope to get out of this course? What’s one problem / curiosity / issue that sometimes keeps you up at night?

5. Outline A quick history lesson Visual Analytics: a definition Why Visual Analytics? Building blocks: perception Structure of this course Takeaways

6. (Incomplete) History of Visual Analytics: 1970s - CAD/CAM, building cars, planes, chips - Starting to think about: 3D, animation, edu, medicine

7. (Incomplete) History of Visual Analytics: 1980s - Scientific visualization, physical phenomena - Starting to think about: photorealism, entertainment

8. (Incomplete) History of Visual Analytics: 1990s - Information visualization, storytelling - Starting to think about: online spaces, interaction

9. (Incomplete) History of Visual Analytics: 2000s - Coordination across multiple views, interaction - Starting to think about: sensemaking, provenance

10. (Incomplete) History of Visual Analytics Early 2000s: US is reacting to 9/11 2003: Dept. of Homeland Security (DHS) est. DHS Goals: - Prevent terrorist attacks within the US - Reduce US vulnerability to terrorism - Minimize damage / aid recovery from attacks that do occur 2005: DHS charters the National Visualization and Analytics Center (NVAC) at PNNL

11. (Incomplete) History of Visual Analytics NVAC mission: Develop advanced information technologies to support the Homeland Security mission with data that is massive, complex, incomplete, and uncertain in scenarios requiring human judgment. Challenges: How do we support analytical reasoning under complex, changing circumstances? How do we make use of domain expertise, when domain experts are not computer scientists? New idea: (visualization) ∩ (analytics)

12. Visualization (def.) Creating visual representations of data to reinforce human cognition

13. Analytics (def.) Discovery and communication of meaningful patterns in data

14. Visual Analytics (def.) “The science of analytical reasoning facilitated by interactive visual interfaces” 1 1 Thomas, James J., and Kristin A. Cook, eds. Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society Press, 2005.

15. What is Visual Analytics? Visualization plus… data representation interaction & analysis dissemination & story telling a scientific approach (evaluation) US Congress: “Visual analytics provides the last 12 inches between the masses of information and the human mind to make decisions.”

16. Examples of Visual Analytics Systems (Financial Fraud) Wire Fraud Detection –With Bank of America –Hundreds of thousands of transactions per day Global Terrorism –Application of the investigative 5 W’s Bridge Maintenance –With US DOT –Exploring subjective inspection reports Biomechanical Motion –Interactive motion comparison methods Chang, Remco, et al. "Scalable and interactive visual analysis of financial wire transactions for fraud detection." Information visualization 7.1 (2008): 63-76.

17. Examples of Visual Analytics Systems (Analysis of Civil Unrest) Wire Fraud Detection –With Bank of America –Hundreds of thousands of transactions per day Global Terrorism –Application of the investigative 5 W’s Bridge Maintenance –With US DOT –Exploring subjective inspection reports Biomechanical Motion –Interactive motion comparison methods Godwin, Alex, et al. "Visual analysis of entity relationships in the Global Terrorism Database." SPIE Defense and Security Symposium. International Society for Optics and Photonics, 2008.

18. Examples of Visual Analytics Systems (Transportation Analysis) Wire Fraud Detection –With Bank of America –Hundreds of thousands of transactions per day Global Terrorism –Application of the investigative 5 W’s Bridge Maintenance –With US DOT –Exploring subjective inspection reports Biomechanical Motion –Interactive motion comparison methods Wang, Xiaoyu, et al. "An interactive visual analytics system for bridge management." Computer Graphics Forum. Vol. 29. No. 3. Blackwell Publishing Ltd, 2010.

19. Examples of Visual Analytics Systems (Biomechanical Motion) Wire Fraud Detection –With Bank of America –Hundreds of thousands of transactions per day Global Terrorism –Application of the investigative 5 W’s Bridge Maintenance –With US DOT –Exploring subjective inspection reports Biomechanical Motion –Interactive motion comparison methods Spurlock, Scott, et al. "Combining automated and interactive visual analysis of biomechanical motion data." Advances in Visual Computing. Springer Berlin Heidelberg, 2010. 564-573.

20. Other Examples of Visual Analytics Systems (Pandemic / Healthcare) Healthcare –Unreliable data sources –Spatiotemporal analysis Network Security –Large amounts of transactional data Energy / Power Grid –Graph-based visualization –Identifies failure points in the system Multimedia Analysis –Text analysis –Image and video analysis Maciejewski, Ross, et al. "A visual analytics approach to understanding spatiotemporal hotspots." Visualization and Computer Graphics, IEEE Transactions on 16.2 (2010): 205-220.

21. Other Examples of Visual Analytics Systems (Network Security) Healthcare –Unreliable data sources –Spatiotemporal analysis Network Security –Large amounts of transactional data Energy / Power Grid –Graph-based visualization –Identifies failure points in the system Multimedia Analysis –Text analysis –Image and video analysis Interactive Wormhole Detection in Large Scale Wireless Networks, Weichao Wang, Aidong Lu, Proceedings of IEEE Symposium on Visual Analytics Science and Technology (VAST), pp.99-106, 2006.

22. Other Examples of Visual Analytics Systems (Energy / Power Grid) Healthcare –Unreliable data sources –Spatiotemporal analysis Network Security –Large amounts of transactional data Energy / Power Grid –Graph-based visualization –Identifies failure points in the system Multimedia Analysis –Text analysis –Image and video analysis Wong, Pak Chung, et al. "A novel visualization technique for electric power grid analytics." Visualization and Computer Graphics, IEEE Transactions on 15.3 (2009): 410-423.

23. Other Examples of Visual Analytics Systems (Multimedia Analysis) Healthcare –Unreliable data sources –Spatiotemporal analysis Network Security –Large amounts of transactional data Energy / Power Grid –Graph-based visualization –Identifies failure points in the system Multimedia Analysis –Text analysis –Image and video analysis H. Luo, et al., ``Integrating Multi-Modal Content Analysis and Hyperbolic Visualization for Large-Scale News Videos Retrieval and Exploration.” Signal Processing: Image Communication, vol.23, no.8, pp.538-553, 2008.

24. Why Visual Analytics? We are collecting and generating data faster than traditional methods can keep up This data is often complex, ambiguous, noisy  Not only is the data unmanageably big, it requires human interpretation and understanding  Oh, great Major problem: humans don’t scale, either

25. VA: Human + Machine Collaboration Make use of what both humans and machines bring to the table using visual interfaces as a medium Key considerations: - Traditional analytics (stats, machine learning) can help make massive data tractable - We can use what we know about cognition/perception to help analysts put data in context

26. Perception: Preattentive Processing First impressions matter: <200ms of visual stimulation Performed in parallel across the entire visual field Detects basic features such as: – Color – Intensity – Size – Density – Line termination – Intersection Facilitates several important tasks: – Target detection (presence or absence) – Boundary detection / grouping – Region tracking – Counting and estimation –Curvature –Closure –Tilt –Light source direction –Flicker –Velocity

27. Perception: Preattentive Processing

28. What did you see?

29. Perception: Preattentive Processing Whatever draws our eyes draws our attention In visual analytics, we can use preattentive processing to our advantage: - Alerts - Anomalies - Situational awareness However, this same processing can also be problematic: - Artifacts - Visual distractors - Change blindness

30. Perception: Preattentive Processing

31. Takeaways: Perception Not just pretty pictures! There are compelling cognitive reasons why some visualization techniques are helpful and others… not so much Low-level decisions about visual mappings can have a significant effect on overall performance - Analogous to design choices in algorithms, etc. - Need to understand the impact on efficiency and accuracy/integrity - Manage the analyst’s cognitive burden

32. What We’ll Cover in Lecture Next Class: Mental and Visualization Models Unit 1: Data Wrangling - Data Collection and Cleaning - Analysis Tools - Data Modeling Unit 2: Visualization Techniques - Introduction to Visualization - Visual Mapping - Data Projections - Interaction - Storytelling with Visual Analytics Unit 3: Advanced Topics - Practical Challenges in Building VA Systems - Analytic Provenance - Evaluation Techniques - Open Research Topics

33. Structure of This Course Disclaimer: this class is an experiment in constructionism (the idea that people learn most effectively when they’re building meaningful things) My focus as an instructor: - Expose you to some foundational principles and available tools - Ask questions that build your intuitions about identifying problems where VA techniques can help - Most important: help you find opportunities to solve real problems in areas YOU care about (and hopefully learn some cool stuff about VA along the way…)

34. Guest Tutorials and Demonstrations Maja Milosevjivic, MITLL / Smith College - Introduction to R Lane Harrison, Tufts / UNCC - Crash Course in D3.js Megan Monroe, IBM / UMD - EventFlow and Semantic Interaction Rajmonda Caceres, MITLL / UIC - Data Projections Diane Staheli, MITLL - Conducting a Needs Assessment …and more TBD!

35. Assignments and Grading Participation (20%): show up, engage, and you’ll be fine 3 (short) assignments (30%): built to help you become comfortable with the techniques we discuss in class 10-minute presentation on a research paper (10%): get a sense for what’s out there in the Visual Analytics world Course project (40%)

36. Course Project Various industry partners (VIPs) will be pitching potential datasets and/or analysis questions You’re also welcome to propose your own! Goals: - Learn how to break big, unwieldy questions down into clear, manageable problems - Figure out if/how the techniques we cover in class apply to your specific problems - Build VA systems to address them Several (graded) milestones along the way Demos and reception on the final day of class gain real experience | solve real problems build real relationships

37. What You’ll Get By the end of this course, you will: Understand what Visual Analytics is Know the foundational methods and tools available Be familiar with some ongoing research in VA Know how to perform analytical tasks and report your findings Have access to guest speakers from really cool places (IBM, Google, Yelp, and more…) Have (marketable!) experience developing useful visual analytics applications for real clients

38. What I Expect from You You enjoy grappling with difficult problems, and you’re excited about “figuring stuff out” You are (or are willing to work to become) proficient in programming and debugging We’ll do crash courses in various tools and environments, but this is NOT a course in learning general programming techniques You’re welcome to work in whatever language(s) you prefer, but I’m more helpful in the ones I know You’re comfortable asking questions

39. What You Can Expect from Me I’m flexible w.r.t. the topics we cover: - This course is a collaboration - If there’s something you want to learn that’s not on the agenda, speak up! I’m happy to share my professional connections: - If there’s a company or school you’d like to work with for your project, let me know and I’ll reach out to them - Note: the best way to get a job/internship/etc. is to convince someone on the inside that you’re awesome Downside: I have non-standard office hours - Full-time research scientist at MIT; hours at Tufts are limited - My commitment: if you email me during business hours, you’ll get a response by the end of the day - We can explore other options as needed (Google hangout, etc.)

40. General Information Course website: http://www.cs.tufts.edu/comp/150VIZ Syllabus Textbooks (only one required, free download) Assignments Grading Accommodations

41. Questions?