Best keyword cover search

Deng, Ke, Li, Xin, Lu, Jiaheng and Zhou, Xiaofang (2015) Best keyword cover search. IEEE Transactions on Knowledge and Data Engineering, 27 1: 61-73. doi:10.1109/TKDE.2014.2324897

Author Deng, Ke
Li, Xin
Lu, Jiaheng
Zhou, Xiaofang
Title Best keyword cover search
Journal name IEEE Transactions on Knowledge and Data Engineering   Check publisher's open access policy
ISSN 1041-4347
Publication date 2015-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.1109/TKDE.2014.2324897
Open Access Status
Volume 27
Issue 1
Start page 61
End page 73
Total pages 13
Place of publication Piscataway NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2015
Language eng
Abstract It is common that the objects in a spatial database (e.g., restaurants/hotels) are associated with keyword(s) to indicate their businesses/services/features. An interesting problem known as Closest Keywords search is to query objects, called keyword cover, which together cover a set of query keywords and have the minimum inter-objects distance. In recent years, we observe the increasing availability and importance of keyword rating in object evaluation for the better decision making. This motivates us to investigate a generic version of Closest Keywords search called Best Keyword Cover which considers inter-objects distance as well as the keyword rating of objects. The baseline algorithm is inspired by the methods of Closest Keywords search which is based on exhaustively combining objects from different query keywords to generate candidate keyword covers. When the number of query keywords increases, the performance of the baseline algorithm drops dramatically as a result of massive candidate keyword covers generated. To attack this drawback, this work proposes a much more scalable algorithm called keyword nearest neighbor expansion (keyword-NNE). Compared to the baseline algorithm, keyword-NNE algorithm significantly reduces the number of candidate keyword covers generated. The in-depth analysis and extensive experiments on real data sets have justified the superiority of our keyword-NNE algorithm.
Keyword Spatial database
Keyword cover
Keyword rating
Point of interests
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 4 Dec 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
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