Publication Type

Journal Article

Version

Postprint

Publication Date

2015

Abstract

The diffusion of social networks introduces new challenges and opportunities for advanced services, especially so with their ongoing addition of location-based features. We show how applications like company and friend recommendation could significantly benefit from incorporating social and spatial proximity, and study a query type that captures these two-fold semantics. We develop highly scalable algorithms for its processing, and enhance them with elaborate optimizations. Finally, we use real social network data to empirically verify the efficiency and efficacy of our solutions.

Keywords

Data structures, Distributed databases, Educational institutions, Euclidean distance, Indexes, Social network services, Tin

Discipline

Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

IEEE Transactions on Knowledge and Data Engineering (TKDE)

ISSN

1041-4347

Identifier

10.1109/TKDE.2014.2339838

Publisher

IEEE

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1109/TKDE.2014.2339838

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