Event-based Visual Analytics for VAST Challenge 2014

We present an event-based approach for solving a directed sensemaking task in which we combine powerful information foraging tools with intuitive synthesis spaces to solve the VAST 2014 Mini-Challenge 1 (MC1). A combination of student-created and commericially available software are used to solve various aspects of the scenario. In addition to applying entitiy extraction and topic modelling, we enable the user to explore a large dataset using multi-model semantic interaction, which infers analytical reasoning from user actions to augment the data spatialization and determine what information should be presented and suggested to the user. Additionally, we visualize extracted topics using Tableau to construct a timeline of events surrounding the questions posed by the challenge.

At the same time, we also report the approach and results to solve the VAST 2014 Mini-Challenge 2 (MC2). Based on the commercial interactive visualization software Tableau, we follow the notional model of sensemaking loop for analysis of the massive multi-dimensional, multi-source and time-varying data sets in MC2. Our findings show that we can effectively identify the commonalities and anomalies to understand the GAStech employees' daily life.

AUTHORS

Ji Wang, Department of Computer Science, Virginia Tech
Lauren Bradel, Department of Computer Science, Virginia Tech
Peng Mi, Department of Computer Science, Virginia Tech
Chris North, Department of Computer Science, Virginia Tech

POSTER

Link: http://people.cs.vt.edu/~wji/VAST2014_poster.pdf

SUMMARY PAPERS

MC1: http://people.cs.vt.edu/~wji/VAST_2014_MC1_VT_Wang.pdf
MC2: http://people.cs.vt.edu/~wji/VAST_2014_MC2_VT_Wang.pdf

VIDEOS

MC1: http://youtu.be/rvYQ4nuwcTQ
MC2: http://youtu.be/r8fxRitnPVU

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