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(Big Data Analytics for Everyone)
Remco Chang
Assistant ProfessorDepartment of Computer Science
Tufts University
Big Data Visual Analytics:A User-Centric Approach
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“The computer is incredibly fast, accurate, and stupid. Man is unbelievably slow, inaccurate, and
brilliant. The marriage of the two is a force beyond calculation.”
-Leo Cherne, 1977 (often attributed to Albert Einstein)
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Work Distribution
Crouser et al., Balancing Human and Machine Contributions in Human Computation Systems. Human Computation Handbook, 2013Crouser et al., An affordance-based framework for human computation and human-computer collaboration. IEEE VAST, 2012
Creativity
Perception
Domain Knowledge
Data ManipulationStorage and Retrieval
Bias-Free Analysis
LogicPrediction
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Visual Analytics = Human + Computer
• Visual analytics is “the science of analytical reasoning facilitated by visual interactive interfaces.”
1. Thomas and Cook, “Illuminating the Path”, 2005.2. Keim et al. Visual Analytics: Definition, Process, and Challenges. Information Visualization, 2008
Interactive Data Exploration
Automated Data Analysis
Feedback Loop
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Visual Analytics Systems
• Political Simulation– Agent-based analysis– With DARPA
• Wire Fraud Detection– With Bank of America
• Bridge Maintenance – With US DOT– Exploring inspection
reports
• Biomechanical Motion– Interactive motion
comparisonCrouser et al., Two Visualization Tools for Analysis of Agent-Based Simulations in Political Science. IEEE CG&A, 2012
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Visual Analytics Systems
R. Chang et al., WireVis: Visualization of Categorical, Time-Varying Data From Financial Transactions, VAST 2008.
• Political Simulation– Agent-based analysis– With DARPA
• Wire Fraud Detection– With Bank of America
• Bridge Maintenance – With US DOT– Exploring inspection
reports
• Biomechanical Motion– Interactive motion
comparison
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Visual Analytics Systems
R. Chang et al., An Interactive Visual Analytics System for Bridge Management, Journal of Computer Graphics Forum, 2010.
• Political Simulation– Agent-based analysis– With DARPA
• Wire Fraud Detection– With Bank of America
• Bridge Maintenance – With US DOT– Exploring inspection
reports
• Biomechanical Motion– Interactive motion
comparison
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Visual Analytics Systems
R. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data, IEEE Vis (TVCG) 2009.
• Political Simulation– Agent-based analysis– With DARPA
• Wire Fraud Detection– With Bank of America
• Bridge Maintenance – With US DOT– Exploring inspection
reports
• Biomechanical Motion– Interactive motion
comparison
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Current Big Data Practice
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Human+Computer in Big Data Analytics
• Goal: Allow an analyst (user) to fluidly explore and analyze a large remote data warehouse from commodity hardware
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Problem: Big Data is BIG and Far Away
Visualization on aCommodity Hardware
Large Data in aData Warehouse
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Approach: Predictive Prefetching
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Predict User Behavior from User Interactions?
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Experiment: Finding Waldo
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Predicting a User’s Completion Time
Fast completion time Slow completion time
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Analyses Results: Performance
Biometric (low-level mouse data)
Accuracy: ~70%
Interaction pattern (high-level button clicks)
Accuracy: ~80%
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Predicting a User’s Personality
External Locus of Control Internal Locus of Control
Ottley et al., How locus of control influences compatibility with visualization style. IEEE VAST , 2011.Ottley et al., Understanding visualization by understanding individual users. IEEE CG&A, 2012.
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Analysis Results: Personality Traits
• Noisy data, but can detect the users’ individual traits “Extraversion”, “Neuroticism”, and “Locus of Control” at ~60% accuracy by analyzing the user’s interactions alone.
Predicting user’s “Extraversion”
Accuracy: ~60%
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• Developed a prototype system (ForeCache) in collaboration with the Big Data Center at MIT and researchers at Brown
• Evaluated system with domain scientists using the NASA MODIS dataset (multi-sensory satellite imagery)
• Remote analysis on commodity hardware shows (near) real-time interactive analysis
Wrap Up: Theory Into Practice