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Scalable diversification of multiple search results

Published: 27 October 2013 Publication History

Abstract

The explosion of big data emphasizes the need for scalable data diversification, especially for applications based on web, scientific, and business databases. However, achieving effective diversification in a multi-user environment is a rather challenging task due to the inherent high processing costs of current data diversification techniques. In this paper, we address the concurrent diversification of multiple search results using various approximation techniques that provide orders of magnitude reductions in processing cost, while maintaining comparable quality of diversification as compared to sequential methods. Our extensive experimental evaluation shows the scalability exhibited by our proposed methods under various workload settings.

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Cited By

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  • (2018)RC-indexProceedings of the VLDB Endowment10.14778/3192965.319296911:7(773-786)Online publication date: 1-Mar-2018
  • (2015)Diversifying with Few Regrets, But too Few to MentionProceedings of the Second International Workshop on Exploratory Search in Databases and the Web10.1145/2795218.2795225(27-32)Online publication date: 31-May-2015
  • (2015)Diversity-Aware Top-k Publish/Subscribe for Text StreamProceedings of the 2015 ACM SIGMOD International Conference on Management of Data10.1145/2723372.2749451(347-362)Online publication date: 27-May-2015
  • Show More Cited By

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cover image ACM Conferences
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
October 2013
2612 pages
ISBN:9781450322638
DOI:10.1145/2505515
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

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Author Tags

  1. algorithms
  2. design
  3. experimentation
  4. performance

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  • Research-article

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CIKM'13
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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management
October 27 - November 1, 2013
California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2018)RC-indexProceedings of the VLDB Endowment10.14778/3192965.319296911:7(773-786)Online publication date: 1-Mar-2018
  • (2015)Diversifying with Few Regrets, But too Few to MentionProceedings of the Second International Workshop on Exploratory Search in Databases and the Web10.1145/2795218.2795225(27-32)Online publication date: 31-May-2015
  • (2015)Diversity-Aware Top-k Publish/Subscribe for Text StreamProceedings of the 2015 ACM SIGMOD International Conference on Management of Data10.1145/2723372.2749451(347-362)Online publication date: 27-May-2015
  • (2015)Progressive diversification for column-based data exploration platforms2015 IEEE 31st International Conference on Data Engineering10.1109/ICDE.2015.7113295(327-338)Online publication date: Apr-2015
  • (2015)Proceedings of the 2015 ACM SIGMOD International Conference on Management of DataundefinedOnline publication date: 27-May-2015
  • (2014)DivIDEProceedings of the 26th International Conference on Scientific and Statistical Database Management10.1145/2618243.2618253(1-12)Online publication date: 30-Jun-2014

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