DIVe: Diversifying view recommendation for visual data exploration

Rischan Mafrur, Mohamed A. Sharaf, Hina A. Khan

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

9 Citations (Scopus)

Abstract

To support effective data exploration, there has been a growing interest in developing solutions that can automatically recommend data visualizations that reveal important data-driven insights. In such solutions, a large number of possible data visualization views are generated and ranked according to some metric of importance, then the top-k most important views are recommended. However, one drawback of that approach is that it often recommends similar views, leaving the data analyst with a limited amount of gained insights. To address that limitation, in this work we posit that employing diversification techniques in the process of view recommendation allows eliminating that redundancy and provides a concise coverage of the possible insights to be discovered. To that end, we propose a hybrid objective utility function, which captures both the importance, as well as the diversity of the insights revealed by the recommended views. While in principle, traditional diversification methods provide plausible solutions under our proposed utility function, they suffer from a significantly high query processing cost. In particular, directly applying such methods leads to a process-first-diversify-next approach, in which all possible data visualization are generated first via executing a large number of aggregate queries. To address that challenge, we propose the DiVE scheme, which efficiently selects the top-k recommended view based on our hybrid utility function. DiVE leverages the properties of both the importance and diversity metrics to prune a large number of query executions without compromising the quality of recommendations. Our experimental evaluation on real datasets shows the performance gains provided by DiVE.

Original languageEnglish
Title of host publicationCIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
EditorsNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
PublisherAssociation for Computing Machinery
Pages1123-1132
Number of pages10
ISBN (Electronic)9781450360142
DOIs
Publication statusPublished - Oct 17 2018
Externally publishedYes
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: Oct 22 2018Oct 26 2018

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference27th ACM International Conference on Information and Knowledge Management, CIKM 2018
Country/TerritoryItaly
CityTorino
Period10/22/1810/26/18

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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