Incremental Evaluation of Top-κ Combinatorial Metric Skyline Query

Author

Tao Jiang
Bin Zhang
Dan Lin, Missouri University of Science and TechnologyFollow
Yunjun Gao
Qing Li

Abstract

In this paper, we define a novel type of skyline query, namely top-κ combinatorial metric skyline (κCMS) query. The κCMS query aims to find k combinations of data points according to a monotonic preference function such that each combination has the query object in its metric skyline. The κCMS query will enable a new set of location-based applications that the traditional skyline queries cannot offer. To answer the κCMS query, we propose two efficient query algorithms, which leverage a suite of techniques including the sorting and threshold mechanisms, reusing technique, and heuristics pruning to incrementally and quickly generate combinations of possible query results. We have conducted extensive experimental studies, and the results demonstrate both effectiveness and efficiency of our proposed algorithms.

Recommended Citation

T. Jiang et al., "Incremental Evaluation of Top-κ Combinatorial Metric Skyline Query," Knowledge-Based Systems, vol. 74, pp. 89 - 105, Elsevier, Jan 2015.

The definitive version is available at https://doi.org/10.1016/j.knosys.2014.11.009

Department(s)

Computer Science

Comments

National Natural Science Foundation of China, Grant LY14F020038

Keywords and Phrases

Algorithm; Combinational skyline; Metric skyline; Query processing; Spatial database

International Standard Serial Number (ISSN)

0950-7051

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

01 Jan 2015