Publication Type
Journal Article
Version
acceptedVersion
Publication Date
3-2015
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 | Numerical Analysis and Scientific Computing
Research Areas
Intelligent Systems and Optimization
Publication
IEEE Transactions on Knowledge and Data Engineering (TKDE)
Volume
27
Issue
3
First Page
781
Last Page
793
ISSN
1041-4347
Identifier
10.1109/TKDE.2014.2339838
Publisher
IEEE
Citation
MOURATIDIS, Kyriakos; LI, Jing; TANG, Yu; and MAMOULIS, Nikos.
Joint search by social and spatial proximity. (2015). IEEE Transactions on Knowledge and Data Engineering (TKDE). 27, (3), 781-793.
Available at: https://ink.library.smu.edu.sg/sis_research/2258
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/TKDE.2014.2339838
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons