http://poloclub.gatech.edu/cse6242CSE6242 / CX4242: Data & Visual Analytics

Analytics Building Blocks

Duen Horng (Polo) Chau Assistant ProfessorAssociate Director, MS AnalyticsGeorgia Tech

Partly based on materials by Professors Guy Lebanon, Jeffrey Heer, John Stasko, Christos Faloutsos

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What is Data & Visual Analytics?

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What is Data & Visual Analytics?

No formal definition!

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Polo’s definition: the interdisciplinary science of combining computation techniques and interactive visualization to transform and model data to aid discovery, decision making, etc.

What is Data & Visual Analytics?

No formal definition!

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What are the “ingredients”?

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What are the “ingredients”?

Need to worry (a lot) about: storage, complex system design, scalability of algorithms, visualization techniques, interaction techniques, statistical tests, etc.

Wasn’t this complex before this big data era. Why?

4http://spanning.com/blog/choosing-between-storage-based-and-unlimited-storage-for-cloud-data-backup/

What is big data? Why should you care?(“big data” is buzz word, so is “IoT” - Internet of Things)

• large amount of info

• Healthcare (EHRs; mobile health sensor data)

• personal history; sickness

• Sports (baseball, basketball, soccer)

• Navigation and maps

• Business (“need analysis”: infer what clients want)

• Social media

• Sensor networks

• stock market (high frequency trading)

• supply chain logistics

(From previous class)What is big data? Why care?

• Many companies’ businesses are based on big data (Google, Facebook, Amazon, Apple, Symantec, LinkedIn, and many more)

• Web search

• Rank webpages (PageRank algorithm)

• Predict what you’re going to type

• Advertisement (e.g., on Facebook)

• Infer users’ interest; show relevant ads

• Infer what you like, based on what your friends like

• Recommendation systems (e.g., Netflix, Pandora, Amazon)

• Online education

• Health IT: patient records (EMR)

• Bio and Chemical modeling:

• Finance

• Cybersecruity

• Internet of Things (IoT)

Good news! Many jobs!Most companies are looking for “data scientists”

The data scientist role is critical for organizations looking to extract insight from information assets for ‘big data’ initiatives and requires a broad combination of skills that may be fulfilled better as a team- Gartner (http://www.gartner.com/it-glossary/data-scientist)

Breadth of knowledge is important.

This course helps you learn some important skills.

Analytics Building Blocks

Collection

Cleaning

Integration

Visualization

Analysis

Presentation

Dissemination

Building blocks, not “steps”• Can skip some

• Can go back (two-way street)

• Examples

• Data types inform visualization design

• Data informs choice of algorithms

• Visualization informs data cleaning (dirty data)

• Visualization informs algorithm design (user finds that results don’t make sense)

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Cleaning

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How big data affects the process?The Vs of big data (3Vs, 4Vs, now 7Vs)

Volume: “billions”, “petabytes” are common

Velocity: think Twitter, fraud detection, etc.

Variety: text (webpages), video (youtube)…

Veracity: uncertainty of data

Variability

Visualization

Value

Collection

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Disseminationhttp://www.ibmbigdatahub.com/infographic/four-vs-big-data

http://dataconomy.com/seven-vs-big-data/

Gartner's 2015 Hype Cycle http://www.gartner.com/newsroom/id/3114217

Schedule

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Two Example Projects (from our research)

NetProbe: Fraud Detection in Online Auction

NetProbe: A Fast and Scalable System for Fraud Detection in Online Auction Networks. Shashank Pandit, Duen Horng (Polo) Chau, Samuel Wang, Christos Faloutsos. WWW 2007

Find bad sellers (fraudsters) on eBay who don’t deliver their items

NetProbe: The Problem

Buyer

$$$

Seller

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Auction fraud is #3 online crime in 2010source: www.ic3.gov

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NetProbe: Key Ideas§ Fraudsters fabricate their reputation by

“trading” with their accomplices§ Fake transactions form near bipartite cores§ How to detect them?

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NetProbe: Key IdeasUse Belief Propagation

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F A HFraudster

AccompliceHonest

Darker means more likely

NetProbe: Main Results

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“Belgian Police”

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What did NetProbe go through?

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Scraping (built a “scraper”/“crawler”)

Design detection algorithm

Not released

Paper, talks, lectures

Apolo Graph Exploration: Machine Learning + Visualization

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Apolo: Making Sense of Large Network Data by Combining Rich User Interaction and Machine Learning. Duen Horng (Polo) Chau, Aniket Kittur, Jason I. Hong, Christos Faloutsos. CHI 2011.

Finding More Relevant Nodes

HCIPaper

Data MiningPaper

Citation network

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Finding More Relevant Nodes

HCIPaper

Data MiningPaper

Citation network

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Finding More Relevant Nodes

Apolo uses guilt-by-association(Belief Propagation)

HCIPaper

Data MiningPaper

Citation network

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Demo: Mapping the Sensemaking Literature

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Nodes: 80k papers from Google Scholar (node size: #citation) Edges: 150k citations

Key Ideas (Recap)Specify exemplarsFind other relevant nodes (BP)

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What did Apolo go through?

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Scrape Google Scholar. No API :(

Design inference algorithm (Which nodes to show next?)

Paper, talks, lectures

Interactive visualization you just saw

Homework 1 (out next week; tasks subject to change)

• Simple “End-to-end” analysis

• Collect data using API)

• Movies (Actors, directors, related movies, etc.)

• Store in SQLite database

• Transform data to movie-movie network

• Analyze, using SQL queries (e.g., create graph’s degree distribution)

• Visualize, using Gephi

• Describe your discoveries

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