Lytic: synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics

Authors:
Edward Clarkson;Jaegul Choo;John Turgeson;Ray Decuir;Haesun Park
Affiliations:
Georgia Tech Research Institute, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA;Georgia Tech Research Institute, Atlanta, GA;Georgia Tech Research Institute, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA
Venue:
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
Year:
2013

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Abstract

We present Lytic, a domain-independent, faceted visual analytic (VA) system for interactive exploration of large datasets. It combines a flexible UI that adapts to arbitrary character-separated value (CSV) datasets with algorithmic preprocessing to compute unsupervised dimension reduction and cluster data from high-dimensional fields. It provides a variety of visualization options that require minimal user effort to configure and a consistent user experience between visualization types and underlying datasets. Filtering, comparison and visualization operations work in concert, allowing users to hop seamlessly between actions and pursue answers to expected and unexpected data hypotheses.