Efficient Recommendation of Aggregate Data Visualizations

Humaira Ehsan, Mohamed A. Sharaf, Panos K. Chrysanthis

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Data visualization is a common and effective technique for data exploration. However, for complex data, it is infeasible for an analyst to manually generate and browse all possible visualizations for insights. This observation motivated the need for automated solutions that can effectively recommend such visualizations. The main idea underlying those solutions is to evaluate the utility of all possible visualizations and then recommend the top-k visualizations. This process incurs high data processing cost, that is further aggravated by the presence of numerical dimensional attributes. To address that challenge, we propose novel view recommendation schemes, which incorporate a hybrid multi-objective utility function that captures the impact of numerical dimension attributes. Our first scheme, Multi-Objective View Recommendation for Data Exploration (MuVE), adopts an incremental evaluation of our multi-objective utility function, which allows pruning of a large number of low-utility views and avoids unnecessary objective evaluations. Our second scheme, upper MuVE (uMuVE), further improves the pruning power by setting the upper bounds on the utility of views and allowing interleaved processing of views, at the expense of increased memory usage. Finally, our third scheme, Memory-aware uMuVE (MuMuVE), provides pruning power close to that of uMuVE, while keeping memory usage within a pre-specified limit.

Original languageEnglish
Article number80818256
Pages (from-to)263-277
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume30
Issue number2
DOIs
Publication statusPublished - Feb 1 2018
Externally publishedYes

Keywords

  • Data exploration
  • aggregate views
  • view recommendation
  • visual analytics

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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