Joint Search by Social and Spatial Proximity [Extended Abstract]

Author

Kyriakos MOURATIDIS, Singapore Management UniversityFollow
Jing LI, University of Hong Kong
Yu TANG, University of Hong Kong
Nikos MAMOULIS, University of Hong Kong

Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2016

Abstract

The diffusion of social networks introduces new challengesand opportunities for advanced services, especially so with their ongoingaddition of location-based features. We show how applications like company andfriend recommendation could significantly benefit from incorporating social andspatial proximity, and study a query type that captures these twofold semantics.We develop highly scalable algorithms for its processing, and use real socialnetwork data to empirically verify their efficiency and efficacy.

Keywords

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

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

ICDE 2016: IEEE 32nd International Conference on Data Engineering: May 16-20, 2016, Helsinki, Finland: Proceedings

First Page

1578

Last Page

1579

ISBN

9781509020201

Identifier

10.1109/ICDE.2016.7498434

Publisher

IEEE

City or Country

Piscataway, NJ

Citation

MOURATIDIS, Kyriakos; LI, Jing; TANG, Yu; and MAMOULIS, Nikos. Joint Search by Social and Spatial Proximity [Extended Abstract]. (2016). ICDE 2016: IEEE 32nd International Conference on Data Engineering: May 16-20, 2016, Helsinki, Finland: Proceedings. 1578-1579.
Available at: https://ink.library.smu.edu.sg/sis_research/3216

Copyright Owner and License

Authors

Creative Commons License

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

Additional URL

https://doi.org/10.1109/ICDE.2016.7498434