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.
Data structures, Distributed databases, Educational institutions, Euclidean distance, Indexes, Social network services, Tin
Databases and Information Systems
Data Management and Analytics
IEEE Transactions on Knowledge and Data Engineering (TKDE)
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). Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2258
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