Socio-Spatial Group Queries for Impromptu Activity Planning

The development and integration of social networking services and smartphones have made it easy for individuals to organize impromptu social activities anywhere and anytime. Main challenges arising in organizing impromptu activities are mostly due to the requirements of making timely invitations in...

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Bibliographic Details
Published in:arXiv.org 2015-05
Main Authors: Shen, Chih-Ya, De-Nian, Yang, Liang-Hao, Huang, Wang-Chien, Lee, Chen, Ming-Syan
Format: Article
Language:English
Subjects:
Algorithms
Integer programming
Linear programming
Optimization
Pruning
Run time (computers)
Smartphones
Solution space
Space exploration
Online Access:Get full text
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Summary:The development and integration of social networking services and smartphones have made it easy for individuals to organize impromptu social activities anywhere and anytime. Main challenges arising in organizing impromptu activities are mostly due to the requirements of making timely invitations in accordance with the potential activity locations, corresponding to the locations of and the relationship among the candidate attendees. Various combinations of candidate attendees and activity locations create a large solution space. Thus, in this paper, we propose Multiple Rally-Point Social Spatial Group Query (MRGQ), to select an appropriate activity location for a group of nearby attendees with tight social relationships. Although MRGQ is NP-hard, the number of attendees in practice is usually small enough such that an optimal solution can be found efficiently. Therefore, we first propose an Integer Linear Programming optimization model for MRGQ. We then design an efficient algorithm, called MAGS, which employs effective search space exploration and pruning strategies to reduce the running time for finding the optimal solution. We also propose to further optimize efficiency by indexing the potential activity locations. A user study demonstrates the strength of using MAGS over manual coordination in terms of both solution quality and efficiency. Experimental results on real datasets show that our algorithms can process MRGQ efficiently and significantly outperform other baseline algorithms, including one based on the commercial parallel optimizer IBM CPLEX.
ISSN:2331-8422
DOI:10.48550/arxiv.1505.02681