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Dynamic monitoring of optimal locations in road network databases

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Abstract

Optimal location (OL) queries are a type of spatial queries that are particularly useful for the strategic planning of resources. Given a set of existing facilities and a set of clients, an OL query asks for a location to build a new facility that optimizes a certain cost metric (defined based on the distances between the clients and the facilities). Several techniques have been proposed to address OL queries, assuming that all clients and facilities reside in an \(L_p\) space. In practice, however, movements between spatial locations are usually confined by the underlying road network, and hence, the actual distance between two locations can differ significantly from their \(L_p\) distance. Motivated by the deficiency of the existing techniques, this paper presents a comprehensive study on OL queries in road networks. We propose a unified framework that addresses three variants of OL queries that find important applications in practice, and we instantiate the framework with several novel query processing algorithms. We further extend our framework to efficiently monitor the OLs when locations for facilities and/or clients have been updated. Our dynamic update methods lead to efficient answering of continuous optimal location queries. We demonstrate the efficiency of our solutions through extensive experiments with large real data.

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Notes

  1. Note that we cannot apply Blossom on \(G_i\) directly, since a candidate location in \(G_i\) may attract a client outside \(G_i\).

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Acknowledgments

Bin Yao was supported by the NSFC (Grant No. 61202025), the 863 Program of China (Grant No. 2011AA01A202), the Program for Changjiang Scholars and Innovative Research Team in University of China (IRT1158, PCSIRT), Shanghai Excellent Academic Leaders Plan (No. 11XD1402900), Microsoft Research Asia (the Urban Informatics Research Grant), and the STCSM (Grant No. 12ZR1414900). Xiaokui Xiao was supported by the Nanyang Technological University under SUG Grant M58020016 and by Microsoft Research Asia under an Urban Informatics Research Grant. Feifei Li was supported in part by NSF Grant IIS-1251019.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, Shanghai, China

    Bin Yao & Yifan Wu

  2. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Xiaokui Xiao

  3. School of Computing, University of Utah, Salt Lake City, UT, USA

    Feifei Li

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  1. Bin Yao
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  2. Xiaokui Xiao
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  3. Feifei Li
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  4. Yifan Wu
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Correspondence to Feifei Li.

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Yao, B., Xiao, X., Li, F. et al. Dynamic monitoring of optimal locations in road network databases. The VLDB Journal 23, 697–720 (2014). https://doi.org/10.1007/s00778-013-0347-5

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  • DOI: https://doi.org/10.1007/s00778-013-0347-5

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