Explorable Visual Analytics (EVA) Interactive Exploration of LEHD. Saman Amraii - Amir Yahyavi Carnegie Mellon University
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1 Explorable Visual Analytics (EVA) Interactive Exploration of LEHD Saman Amraii - Amir Yahyavi Carnegie Mellon University
2 Motivation Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 2
3 Motivation 1854 Cholera outbreak in Soho, London 616 Dead No Germ Theory Miasmatic Theory (bad air) John Snow Spread through water supply Study of water didn t help Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 3
4 Motivation Drew a map Broad Street Pump Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 4
5 Motivation Solved by removing a handle Political controversy Officials rejected the theory Accepted 12 years later in another outbreak John Snow memorial and public house on Broadwick Street, Soho Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 5
6 Scientific Method START OBSERVATIONS MODELS HYPOTHESIS NULL HYPOTHESIS REFUTE HYPOTHESIS AND MODEL EXPERIMENT INTERPRETATION SUPPORT HYPOTHESIS AND MODEL DON T END HERE Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 6
7 Scientific Method Hypothesis Data Collection Analysis: Interaction, Visualization +Contextual Knowledge New Hypothesis Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 7
8 Scientific Method Data Production >>>>>> Data Consumption Big Data Hypothesis Data Collection Analysis: Interaction, Visualization +Contextual Knowledge New Hypothesis Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 8
9 Sensemaking Loop Repeat Visualization Interaction Contextual Knowledge New Hypothesis This loop should happen fast, otherwise we hesitate to explore or lose our train of thought Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 9
10 The Explorables Collaborative An effort to understand the challenges in visualizing, exploring, and analyzing large and complex data. Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 10
11 Explorable Visual Analytics (EVA) Goal: Improving Hypothesis Generation Easy Exploration Sharing Discoveries Quick Intuitions Testing Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 11
12 Explorable Visual Analytics (EVA) Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 12
13 EVA Demo Data? large, complex, high spatial and temporal resolution Opportunities for real and meaningful discoveries Census Longitudinal Employer-Household Dynamics (LEHD) Contiguous US Hundreds of millions of data points Tens of dimensions 10 years ( ) Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 13
14 Technical Information SERVER CLIENT Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 14
15 Technical Information Big Technical Challenges: Massive size of Data: Big Data Processing on Server + Client Side Analysis Open Source Compatibility: Any Modern Desktop Browser (Any OS, 1~2 GB of RAM) Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 15
16 Key Aspects Improving Hypothesis Generation, How? Scalable: Interactive visualization of large, high-dimensional datasets High Resolution: Don t aggregate if you can Less data loss Intuitive: Intuitive navigation in high dimensional space Responsive: Removing the delay between forming a hypothesis and seeing the visualization Aiding our limited working memory Accessible: Using the web with no additional installation Shareable: Easy to share explorable discoveries and tell a story Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 16
17 Why? DISCOVERY DISSEMINATION Human-Centered Data Mining Curiosity-Driven Discovery Collaborative Exploration Guided Tours Participatory Learning Accessibility Data-Driven Decision Making Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 17
18 Thank You Tuesday, June 23rd 2015 Explorable Visual Analytics (EVA) 18
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