Full Citation

Title: Finding k-Dominant G-Skyline Groups on High Dimensional Data

Citation Type: Journal Article

Publication Year: 2018

ISBN:

ISSN:

DOI: 2169-3536

NSFID:

PMCID:

PMID:

Abstract: Skyline query retrieves a set of skyline points which are not dominated by any other point and has attracted wide attention in database community. Recently, an important variant G-Skyline is developed. It aims to return optimal groups of points. However, when data dimensionality is high, G-Skyline result has too many groups, which makes that users cannot determine which groups are satisfactory. To find less but more representative groups of points, in this paper, we propose a novel concept of k-dominant G-Skyline, which first adopts k-dominance to retrieve more representative points and then computes the groups not k-dominated by others. In addition, we present a two-phase algorithm to efficiently compute k-dominant G-Skyline groups. In the first phase, we construct a lkDG structure while pruning the points never included in any k-dominant G-Skyline group as much as possible. In the second phase, using lkDG, we propose two efficient k-dominant G-Skyline searching methods SM-P and SM-G, which generate new candidate groups from single points and ancestor groups, respectively. Our experimental results indicate that our proposed algorithms are more efficient than the baseline methods on real and synthetic data sets.

Url: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8481680

User Submitted?: No

Authors: Zhang, Kaiqi; Gao, Hong; Han, Xixian; Wang, JinBao

Periodical (Full): IEEE. Translations and content mining

Issue:

Volume: 6

Pages: 58521-58531

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

Countries: