A Fully Dynamic Algorithm for k-Regret Minimizing Sets

Yanhao Wang, Yuchen Li, Raymond Chi-Wing Wong, Kian-Lee Tan

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review


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 k th-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.

Titel på värdpublikation2021 IEEE 37th International Conference on Data Engineering (ICDE)
Antal sidor12
ISBN (tryckt)978-1-7281-9185-0
ISBN (elektroniskt)978-1-7281-9184-3
StatusPublicerad - 2021
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangIEEE International Conference on Data Engineering - Chania, Grekland
Varaktighet: 19 apr. 202123 apr. 2021
Konferensnummer: 37


NamnIEEE International Conference on Data Engineering
ISSN (tryckt)1084-4627


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