Geo-Social Ranking: functions and query processing

Geo-Social Ranking: functions and query processing Given a query location q , Geo-Social Ranking (GSR) ranks the users of a Geo-Social Network based on their distance to q , the number of their friends in the vicinity of q , and possibly the connectivity of those friends. We propose a general GSR framework and four GSR functions that assign scores in different ways: (i) LC, which is a weighted linear combination of social (i.e., friendships) and spatial (i.e., distance to q ) aspects, (ii) RC, which is a ratio combination of the two aspects, (iii) HGS, which considers the number of friends in coincident circles centered at q , and (iv) GST, which takes into account triangles of friends in the vicinity of q . We investigate the behavior of the functions, qualitatively assess their results, and study the effects of their parameters. Moreover, for each ranking function, we design a query processing technique that utilizes its specific characteristics to efficiently retrieve the top- k users. Finally, we experimentally evaluate the performance of the top- k algorithms with real and synthetic datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Geo-Social Ranking: functions and query processing

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-015-0400-7
Publisher site
See Article on Publisher Site

Abstract

Given a query location q , Geo-Social Ranking (GSR) ranks the users of a Geo-Social Network based on their distance to q , the number of their friends in the vicinity of q , and possibly the connectivity of those friends. We propose a general GSR framework and four GSR functions that assign scores in different ways: (i) LC, which is a weighted linear combination of social (i.e., friendships) and spatial (i.e., distance to q ) aspects, (ii) RC, which is a ratio combination of the two aspects, (iii) HGS, which considers the number of friends in coincident circles centered at q , and (iv) GST, which takes into account triangles of friends in the vicinity of q . We investigate the behavior of the functions, qualitatively assess their results, and study the effects of their parameters. Moreover, for each ranking function, we design a query processing technique that utilizes its specific characteristics to efficiently retrieve the top- k users. Finally, we experimentally evaluate the performance of the top- k algorithms with real and synthetic datasets.

Journal

The VLDB JournalSpringer Journals

Published: Dec 1, 2015

References

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