A Branch and Bound Method for Min-dist Location Selection Queries

Given a set of clients and a set of existing facilities, the min-dist location selection query returns a location from a given set of potential locations for establishing a new facility so that the average distance between a client and her nearest facility is minimized. This type of queries has a wide range of applications in marketing, decision support systems, urban development simulations and massively multiplayer online games. The computational cost of a naive algorithm, which sequentially scans all the potential locations, is too high to process this type of queries in real time. Motivated by this, we propose a branch and bound algorithm that exploits geometric properties of the data objects and early prunes unpromising potential locations from consideration to achieve a higher query processing efficiency. We conduct a detailed cost analysis and extensive experiments to validate the efficiency of the branch and bound algorithm. The results show that the proposed algorithm outperforms the naive algorithm constantly.

[1]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[2]  Nick Roussopoulos,et al.  Nearest neighbor queries , 1995, SIGMOD '95.

[3]  King-Ip Lin,et al.  An index structure for efficient reverse nearest neighbor queries , 2001, Proceedings 17th International Conference on Data Engineering.

[4]  Jin Huang,et al.  Top-k most influential locations selection , 2011, CIKM '11.

[5]  Muhammad Aamir Cheema,et al.  Influence zone: Efficiently processing reverse k nearest neighbors queries , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[6]  Yang Du,et al.  The Optimal-Location Query , 2005, SSTD.

[7]  Flip Korn,et al.  Influence sets based on reverse nearest neighbor queries , 2000, SIGMOD 2000.

[8]  Hui Xiong,et al.  High-dimensional kNN joins with incremental updates , 2010, GeoInformatica.

[9]  Kyriakos Mouratidis,et al.  Medoid Queries in Large Spatial Databases , 2005, SSTD.

[10]  S. Muthukrishnan,et al.  Influence sets based on reverse nearest neighbor queries , 2000, SIGMOD '00.

[11]  Yunjun Gao,et al.  Optimal-Location-Selection Query Processing in Spatial Databases , 2009, IEEE Transactions on Knowledge and Data Engineering.

[12]  Dan Lin,et al.  The Min-dist Location Selection Query , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[13]  Raymond Chi-Wing Wong,et al.  A highly optimized algorithm for continuous intersection join queries over moving objects , 2011, The VLDB Journal.

[14]  Kyriakos Mouratidis,et al.  Aggregate nearest neighbor queries in spatial databases , 2005, TODS.

[15]  Marios Hadjieleftheriou,et al.  R-Trees - A Dynamic Index Structure for Spatial Searching , 2008, ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems.

[16]  Wei Wu,et al.  Continuous Reverse k-Nearest-Neighbor Monitoring , 2008, The Ninth International Conference on Mobile Data Management (mdm 2008).

[17]  Philip S. Yu,et al.  Efficient Method for Maximizing Bichromatic Reverse Nearest Neighbor , 2009, Proc. VLDB Endow..

[18]  Katherine L. Morse,et al.  Interest Management in Large-Scale Distributed Simulations , 1996 .

[19]  Elisa Bertino,et al.  Continuous Intersection Joins Over Moving Objects , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[20]  Rui Zhang,et al.  Ranking locations for facility selection based on potential influences , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[21]  Minh Tam Vu.  Optimal location queries on road network , 2011 .

[22]  Stefan Langerman,et al.  Reverse facility location problems , 2005, CCCG.

[23]  Divyakant Agrawal,et al.  Discovery of Influence Sets in Frequently Updated Databases , 2001, VLDB.

[24]  Yufei Tao,et al.  Progressive computation of the min-dist optimal-location query , 2006, VLDB.

[25]  Feifei Li,et al.  Optimal location queries in road network databases , 2011, 2011 IEEE 27th International Conference on Data Engineering.