期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：4
页码：7576
DOI：10.15680/IJIRCCE.2017.05040191
出版社：S&S Publications
摘要：It is common that the objects in a spatial database are associated with keywordto indicate theirbusinesses/services/features. An interesting problem known as Closest Keywords search is to query objects, callednearest keyword search , which together cover a set of query keywords and have the minimum inter-objects distance. Inrecent years, I observe the increasing availability and importance of keyword rating in object evaluation for the betterdecision making. This motivates us to investigate a generic version of Closest Keywords search called Best KeywordCover which considers inter-objects distance as well as the keyword rating of objects. The baseline algorithm isinspired by the methods of Closest Keywords search which is based on exhaustively combining objects from differentquery keywords to generate candidate keyword covers. When the number of query keywords increases, theperformance of the baseline algorithm drops dramatically as a result of massive candidate keyword covers generated.To recover this drawback, this work proposes a much more scalable algorithm called keyword nearest neighborexpansion (keyword-NNE). Compared to the baseline algorithm, keyword-NNE algorithm significantly reduces thenumber of candidate keyword covers generated. The in-depth analysis and extensive experiments on real data sets havejustified the superiority of our keyword-NNE algorithm.
关键词：Spatial database; Point of Interests; Keywords; Keyword Rating; Keyword Cover
