Toward scalable indexing for top-κ queries

Jongwuk Lee, Hyunsouk Cho, Sunyou Lee, Seung Won Hwang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A top-κ query retrieves the best κ tuples by assigning scores for each tuple in a target relation with respect to a user-specific scoring function. This paper studies the problem of constructing an indexing structure for supporting top-κ queries over varying scoring functions and retrieval sizes. The existing research efforts can be categorized into three approaches: list-, layer-, and view-based approaches. In this paper, we mainly focus on the layer-based approach that pre-materializes tuples into consecutive multiple layers. We first propose a dual-resolution layer that consists of coarse-level and fine-level layers. Specifically, we build coarse-level layers using skylines, and divide each coarse-level layer into fine-level sublayers using convex skylines. To make our proposed dual-resolution layer scalable, we then address the following optimization directions: 1) index construction; 2) disk-based storage scheme; 3) the design of the virtual layer; and 4) index maintenance for tuple updates. Our evaluation results show that our proposed method is more scalable than the state-of-the-art methods.

Original languageEnglish
Article number6587712
Pages (from-to)3103-3116
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume26
Issue number12
DOIs
Publication statusPublished - 2014 Dec 1

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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