Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2021

Abstract

Selecting a small set of representatives from a large database is important in many applications such as multi-criteria decision making, web search, and recommendation. The k-regret minimizing set (k-RMS) problem was recently proposed for representative tuple discovery. Specifically, for a large database P of tuples with multiple numerical attributes, the k-RMS problem returns a size-r subset Q of P such that, for any possible ranking function, the score of the top-ranked tuple in Q is not much worse than the score of the kth-ranked tuple in P. Although the k-RMS problem has been extensively studied in the literature, existing methods are designed for the static setting and cannot maintain the result efficiently when the database is updated. To address this issue, we propose the first fully-dynamic algorithm for the k-RMS problem that can efficiently provide the up-to-date result w.r.t. any tuple insertion and deletion in the database with a provable guarantee. Experimental results on several real-world and synthetic datasets demonstrate that our algorithm runs up to four orders of magnitude faster than existing k-RMS algorithms while providing results of nearly equal quality.

Keywords

regret minimizing set, dynamic algorithm, set cover, top-k query, skyline

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of 2021 IEEE 37th International Conference on Data Engineering, Chania, Greece, April 19-22

First Page

1631

Last Page

1642

ISBN

9781728191843

Identifier

10.1109/ICDE51399.2021.00144

Publisher

IEEE

City or Country

USA

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