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

Edward Clarkson, Jaegul Choo, John Turgeson, Ray Decuir, Haesun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings of the ACM SIGKDD 2013 Workshop on Interactive Data Exploration and Analytics, IDEA 2013
PublisherAssociation for Computing Machinery
Pages36-44
Number of pages9
ISBN (Print)9781450323291
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventACM SIGKDD 2013 Workshop on Interactive Data Exploration and Analytics, IDEA 2013 - Chicago, IL, United States
Duration: 2013 Aug 112013 Aug 11

Publication series

NameProceedings of the ACM SIGKDD 2013 Workshop on Interactive Data Exploration and Analytics, IDEA 2013

Conference

ConferenceACM SIGKDD 2013 Workshop on Interactive Data Exploration and Analytics, IDEA 2013
CountryUnited States
CityChicago, IL
Period13/8/1113/8/11

Keywords

  • Infovis
  • Scientific intelligence
  • Visual analytics

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Information Systems

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  • Cite this

    Clarkson, E., Choo, J., Turgeson, J., Decuir, R., & Park, H. (2013). Lytic: Synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics. In Proceedings of the ACM SIGKDD 2013 Workshop on Interactive Data Exploration and Analytics, IDEA 2013 (pp. 36-44). (Proceedings of the ACM SIGKDD 2013 Workshop on Interactive Data Exploration and Analytics, IDEA 2013). Association for Computing Machinery. https://doi.org/10.1145/2501511.2501518