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DiVE: Diversifying View Recommendation for Visual Data Exploration

Published:17 October 2018Publication History

ABSTRACT

To support effective data exploration, there has been a growing interest in developing solutions that can automatically recommend data visualizations that reveal interesting and useful data-driven insights. In such solutions, a large number of possible data visualization views are generated and ranked according to some metric of importance (e.g., a deviation-based metric), 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 good and 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 (e.g., Greedy Construction) 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 an integrated scheme called DiVE, 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.

References

  1. A. M. Albarrak and M. A. Sharaf. 2017. Efficient schemes for similarity-aware refinement of aggregation queries. World Wide Web , Vol. 20, 6 (2017), 1237--1267. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. C. L. A. Clarke et almbox. 2008. Novelty and diversity in information retrieval evaluation. In SIGIR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Drosou and E. Pitoura. 2010. Search result diversification. SIGMOD Record , Vol. 39, 1 (2010), 41--47. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. H. Ehsan et almbox. 2016. MuVE: Efficient Multi-Objective View Recommendation for Visual Data Exploration. In ICDE.Google ScholarGoogle Scholar
  5. H. Ehsan et almbox. 2018. Efficient Recommendation of Aggregate Data Visualizations. TKDE , Vol. 30, 2 (2018), 263--277.Google ScholarGoogle ScholarCross RefCross Ref
  6. R. Fagin et almbox. 2003. Comparing top k lists. In ACM-SIAM.Google ScholarGoogle Scholar
  7. Y. Hu et almbox. 2009. Estimating aggregates in time-constrained approximate queries in Oracle. In EDBT. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Z. Hussain et almbox. 2015. Diversifying with Few Regrets, But too Few to Mention. In ExploreDB. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. I. F. Ilyas et almbox. 2008. A survey of top-k query processing techniques in relational database systems. ACM Comput. Surv. , Vol. 40, 4 (2008), 11:1--11:58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Kandel et almbox. 2012. Profiler: integrated statistical analysis and visualization for data quality assessment. In AVI. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. V. Kantere. 2016. Query Similarity for Approximate Query Answering. In DEXA .Google ScholarGoogle Scholar
  12. V. Kantere et almbox. 2015. Query Relaxation across Heterogeneous Data Sources. In CIKM . Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Key et almbox. 2012. VizDeck: self-organizing dashboards for visual analytics. In SIGMOD. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. H. A. Khan and M. A. Sharaf. 2015. Progressive diversification for column-based data exploration platforms. In ICDE.Google ScholarGoogle Scholar
  15. D. Rafiei et almbox. 2010. Diversifying web search results. In WWW. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. T. Sellam et almbox. 2016. Ziggy: Characterizing Query Results for Data Explorers. PVLDB , Vol. 9, 13 (2016), 1473--1476. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. T. Sellam and M. L. Kersten. 2016. Fast, Explainable View Detection to Characterize Exploration Queries. In SSDBM . Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Seo and B. Shneiderman. 2006. Knowledge Discovery in High-Dimensional Data: Case Studies and a User Survey for the Rank-by-Feature Framework. TVGC , Vol. 12, 3 (2006), 311--322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. B. Smyth et almbox. 2001. Similarity vs. Diversity. In ICCBR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Q. T. Tran and C. Y. Chan. 2010. How to ConQueR why-not questions. In SIGMOD . Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. M. Vartak et almbox. 2014. SEEDB: Automatically Generating Query Visualizations. PVLDB , Vol. 7, 13 (2014), 1581--1584. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. M. Vartak et almbox. 2015. SEEDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics. PVLDB , Vol. 8, 13 (2015), 2182--2193. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. F. B. Viegas et almbox. 2007. Many Eyes: A site for visualization at internet scale . TVGC (2007), 1121--1128. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. M. R. Vieira et almbox. 2011. On query result diversification. In ICDE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. E. Wu et almbox. 2014. The Case for Data Visualization Management Systems. PVLDB , Vol. 7, 10 (2014), 903--906. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. C. Yu et almbox. 2009. It takes variety to make a world: diversification in recommender systems. In EDBT. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M. Zhang and N. Hurley. 2008. Avoiding monotony: improving the diversity of recommendation lists. In RecSys . Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Conferences
      CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
      October 2018
      2362 pages
      ISBN:9781450360142
      DOI:10.1145/3269206

      Copyright © 2018 ACM

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      Publication History

      • Published: 17 October 2018

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      CIKM '18 Paper Acceptance Rate147of826submissions,18%Overall Acceptance Rate1,861of8,427submissions,22%

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