Finding top-k relevant groups of spatial web objects

Finding top-k relevant groups of spatial web objects The web is increasingly being accessed from geo-positioned devices such as smartphones, and rapidly increasing volumes of web content are geo-tagged. In addition, studies show that a substantial fraction of all web queries has local intent. This development motivates the study of advanced spatial keyword-based querying of web content. Previous research has primarily focused on the retrieval of the top-k individual spatial web objects that best satisfy a query specifying a location and a set of keywords. This paper proposes a new type of query functionality that returns top-k groups of objects while taking into account aspects such as group density, distance to the query, and relevance to the query keywords. To enable efficient processing, novel indexing and query processing techniques for single and multiple keyword queries are proposed. Empirical performance studies with an implementation of the techniques and real data suggest that the proposals are viable in practical settings. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Finding top-k relevant groups of spatial web objects

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Publisher
Springer Journals
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-015-0388-z
Publisher site
See Article on Publisher Site

Abstract

The web is increasingly being accessed from geo-positioned devices such as smartphones, and rapidly increasing volumes of web content are geo-tagged. In addition, studies show that a substantial fraction of all web queries has local intent. This development motivates the study of advanced spatial keyword-based querying of web content. Previous research has primarily focused on the retrieval of the top-k individual spatial web objects that best satisfy a query specifying a location and a set of keywords. This paper proposes a new type of query functionality that returns top-k groups of objects while taking into account aspects such as group density, distance to the query, and relevance to the query keywords. To enable efficient processing, novel indexing and query processing techniques for single and multiple keyword queries are proposed. Empirical performance studies with an implementation of the techniques and real data suggest that the proposals are viable in practical settings.

Journal

The VLDB JournalSpringer Journals

Published: Jun 16, 2015

References

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