Spatial-proximity optimization for rapid task group deployment

Chih Ya Shen, De Nian Yang, Wang Chien Lee, Ming Syan Chen

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Spatial proximity is one of the most important factors for the quick deployment of the task groups in various time-sensitive missions. This article proposes a new spatial query, Spatio-Social Team Query (SSTQ), that forms a strong task group by considering (1) the group's spatial distance (i.e., transportation time), (2) skills of the candidate group members, and (3) social rapport among the candidates. Efficient processing of SSTQ is very challenging, because the aforementioned spatial, skill, and social factors need to be carefully examined. In this article, therefore, we first formulate two subproblems of SSTQ, namely Hop-Constrained Team Problem (HCTP) and Connection-Oriented Team Query (COTQ). HCTP is a decision problem that considers only social and skill dimensions. We prove that HCTP is NP-Complete. Moreover, based on the hardness of HCTP, we prove that SSTQ is NP-Hard and inapproximable within any factor. On the other hand, COTQ is a special case of SSTQ that relaxes the social constraint. We prove that COTQ is NP-Hard and propose an approximation algorithm for COTQ, namelyCOTprox. Furthermore, based on the observations on COTprox, we devise an approximation algorithm, SSTprox, with a guaranteed error bound for SSTQ. Finally, to efficiently obtain the optimal solution to SSTQ for small instances, we design two efficient algorithms, SpatialFirst and SkillFirst, with different scenarios in mind. These two algorithms incorporate various effective ordering and pruning techniques to reduce the search space for answering SSTQ. Experimental results on real datasets indicate that the proposed algorithms can efficiently answer SSTQ under various parameter settings.

Original languageEnglish (US)
Article number47
JournalACM Transactions on Knowledge Discovery from Data
Issue number4
StatePublished - Jun 2016

All Science Journal Classification (ASJC) codes

  • General Computer Science


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