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Diversifying with Few Regrets, But too Few to Mention

Published: 31 May 2015 Publication History

Abstract

Representative data provide users with a concise overview of their potentially large query results. Recently, diversity maximization has been adopted as one technique to generate representative data with high coverage and low redundancy. Orthogonally, regret minimization has emerged as another technique to generate representative data with high utility that satisfy the user's preference. In reality, however, users typically have some pre-specified preferences over some dimensions of the data, while expecting good coverage over the other dimensions. Motivated by that need, in this work we propose a novel scheme called ReDi, which aims to generate representative data that balance the tradeoff between regret minimization and diversity maximization. ReDi is based on a hybrid objective function that combines both regret and diversity. Additionally, it employs several algorithms that are designed to maximize that objective function. We perform extensive experimental evaluation to measure the tradeoff between the effectiveness and efficiency provided by the different ReDi algorithms.

References

[1]
S. Chester et al. Computing k-regret minimizing sets. VLDB, 7(5), 2014.
[2]
M. Drosou and E. Pitoura. Search result diversification. SIGMOD Record, 39(1), 2010.
[3]
E. Erkut, Y. Ülküsal, and O. Yeniçerioglu. A comparison of p-dispersion heuristics. Computers & OR, 21(10), 1994.
[4]
R. Fagin, A. Lotem, and M. Naor. Optimal aggregation algorithms for middleware. In PODS, 2001.
[5]
I. F. Ilyas et al. A survey of top-k query processing techniques in relational database systems. ACM Comput. Surv., 40(4), 2008.
[6]
H. A. Khan, M. Drosou, and M. A. Sharaf. Scalable diversification of multiple search results. In CIKM, 2013.
[7]
H. A. Khan and M. Sharaf. Progressive diversification for column-based data exploration platforms. In ICDE, 2015.
[8]
H. A. Khan, M. A. Sharaf, and A. Albarrak. Divide: efficient diversification for interactive data exploration. In SSDBM, 2014.
[9]
D. Nanongkai et al. Regret-minimizing representative databases. VLDB, 3(1-2):1114--1124, 2010.
[10]
P. Peng and R. C.-W. Wong. Geometry approach for k-regret query. In Data Engineering (ICDE), 2014 IEEE 30th International Conference on, pages 772--783. IEEE, 2014.
[11]
A. D. Sarma et al. Beyond skylines and top-k queries: representative databases and e-commerce product search. In CIKM, 2013.
[12]
S. Borzsony, D. Kossmann, and K. Stocker. The skyline operator. In ICDE, 2001.
[13]
Y. Tao et al. Efficient skyline and top-k retrieval in subspaces. IEEE Trans. Knowl. Data Eng., 19(8), 2007.
[14]
M. R. Vieira et al. On query result diversification. In ICDE, 2011.

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cover image ACM Conferences
ExploreDB '15: Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web
May 2015
37 pages
ISBN:9781450337403
DOI:10.1145/2795218
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: 31 May 2015

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  • Research-article
  • Research
  • Refereed limited

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SIGMOD/PODS'15
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SIGMOD/PODS'15: International Conference on Management of Data
May 31 - June 4, 2015
VIC, Melbourne, Australia

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ExploreDB '15 Paper Acceptance Rate 6 of 10 submissions, 60%;
Overall Acceptance Rate 11 of 21 submissions, 52%

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  • (2023)k-Pleased QueryingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313299235:4(4003-4017)Online publication date: 1-Apr-2023
  • (2023)Efficient Diversification for Recommending Aggregate Data VisualizationsIEEE Access10.1109/ACCESS.2023.328345711(62261-62280)Online publication date: 2023
  • (2019)Interactive Data Exploration of Distributed Raw Files: A Systematic Mapping StudyIEEE Access10.1109/ACCESS.2018.28822447(10691-10717)Online publication date: 2019
  • (2019)An experimental survey of regret minimization query and variants: bridging the best worlds between top-k query and skyline queryThe VLDB Journal10.1007/s00778-019-00570-zOnline publication date: 14-Sep-2019
  • (2018)DiVEProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271744(1123-1132)Online publication date: 17-Oct-2018
  • (2016)MPG: Not So Random Exploration of a City2016 17th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM.2016.24(72-81)Online publication date: Jun-2016

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