Lecture 01: Introduction

LECTURE 01:

INTRODUCTION TO VISUAL ANALYTICSJanuary 14, 2015

COMP 150-04Topics in Visual Analytics

COMP 150-12: Topics in Visual Analytics

Fall 2014MW 6:00-7:15pmLecture: Halligan 111ALab: Halligan 118

People

Instructor:R. Jordan [email protected]

Office Hours: W 7:15-8:15pLocation: TBD

Teaching Assistant: TBD

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?

Outline

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

(Incomplete) History of Visual Analytics: 1970s

- CAD/CAM, building cars, planes, chips- Starting to think about: 3D, animation, edu, medicine

(Incomplete) History of Visual Analytics: 1980s

- Scientific visualization, physical phenomena- Starting to think about: photorealism, entertainment

(Incomplete) History of Visual Analytics: 1990s

- Information visualization, storytelling- Starting to think about: online spaces, interaction

(Incomplete) History of Visual Analytics: 2000s

- Coordination across multiple views, interaction- Starting to think about: sensemaking, provenance

(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

(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)

Visualization (def.)

Creating visual representations

of data to reinforce human

cognition

Analytics (def.)

Discovery and communication

of meaningful patterns in data

Visual Analytics (def.)

“The science of analytical reasoning facilitated by interactive visual interfaces”1

1Thomas, James J., and Kristin A. Cook, eds. Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society Press, 2005.

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.”

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.

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.

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.

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 methodsSpurlock, Scott, et al. "Combining automated and interactive visual analysis of biomechanical motion data." Advances in Visual Computing. Springer Berlin Heidelberg, 2010. 564-573.

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.

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.

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.

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

analysisH. 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.

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

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

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

Perception: Preattentive Processing

Perception: Preattentive Processing

• What did you see?

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

Perception: Preattentive Processing

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

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

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…)

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!

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%)

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

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

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

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.)

General Information

• Course website:http://www.cs.tufts.edu/comp/150VIZ

• Syllabus• Textbooks (only one required, free download)• Assignments• Grading• Accommodations

Questions?