A Measure and Conquer Algorithm for the Minimum User Spatial-Aware Interest Group Query Problem

Location-based social networks are important issues in the recent decade. In modern social networks, websites such as Twitter, Facebook, and Plurk, attempt to get the accurate address positions from their users, and try to reduce the gap between virtuality and reality. This paper mainly aims at both the interests of Internet users and their real positions. This issue is called the spatial-aware interest group query problem (SIGQP). Given a user set U with n users, a keywords set W with m words, and a spatial objects set S with s items, each of which contains one or multiple keywords. If a user checks in a certain spatial object, it means the user could be interested in that part of keywords, which is countable to clarify the interests of the user. The SIGQP then tries to find a k-user set \(U_{k}\), \(k \le n\), such that the union of keywords of these k users will equal to W, and additionally, the diameter (longest Euclidean distance of two arbitrary users in \(U_k\)) should be as small as possible. The SIGQP has been proved as NP-Complete, and two heuristic algorithms have been proposed. Extended from SIGQP, the main problem of this paper prioritizes in finding the smallest k for \(U_{k}\) to cover all the keywords, with the users’ distance as the secondary criterion, called as “minimum user spatial-aware interest group query problem” (MUSIGQP). This paper further designs an exact algorithm on a measure-&-conquer-based method to precisely solve this problem, and a performance analysis is given.

[1]  Naphtali Rishe,et al.  Keyword Search on Spatial Databases , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[2]  Cheng Long,et al.  Collective spatial keyword queries: a distance owner-driven approach , 2013, SIGMOD '13.

[3]  Hanan Samet,et al.  Distance browsing in spatial databases , 1999, TODS.

[4]  R. Ravi,et al.  A polylogarithmic approximation algorithm for the group Steiner tree problem , 2000, SODA '98.

[5]  Yufei Tao,et al.  Range search on multidimensional uncertain data , 2007, TODS.

[6]  Christian S. Jensen,et al.  Efficient continuously moving top-k spatial keyword query processing , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[7]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[8]  Chih-Ya Shen,et al.  On socio-spatial group query for location-based social networks , 2012, KDD.

[9]  Hanan Samet,et al.  Incremental distance join algorithms for spatial databases , 1998, SIGMOD '98.

[10]  Christian S. Jensen,et al.  Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects , 2009, Proc. VLDB Endow..

[11]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[12]  Shin'ichi Satoh,et al.  The SR-tree: an index structure for high-dimensional nearest neighbor queries , 1997, SIGMOD '97.

[13]  Mário J. Silva,et al.  Indexing and ranking in Geo-IR systems , 2005, GIR '05.

[14]  Li Lin,et al.  Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering , 2004, IEEE Transactions on Engineering Management.

[15]  Christian S. Jensen,et al.  Spatial Keyword Query Processing: An Experimental Evaluation , 2013, Proc. VLDB Endow..

[16]  Weiwei Sun,et al.  Circle of Friend Query in Geo-Social Networks , 2012, DASFAA.

[17]  Sukho Lee,et al.  Adaptive multi-stage distance join processing , 2000, SIGMOD 2000.

[18]  Yannis Manolopoulos,et al.  Closest pair queries in spatial databases , 2000, SIGMOD 2000.

[19]  Cheng-Te Li,et al.  Team Formation for Generalized Tasks in Expertise Social Networks , 2010, 2010 IEEE Second International Conference on Social Computing.

[20]  João B. Rocha-Junior,et al.  Efficient Processing of Top-k Spatial Keyword Queries , 2011, SSTD.

[21]  Wojciech Rytter,et al.  Fast parallel algorithms for graph matching problems , 1998 .

[22]  Kyriakos Mouratidis,et al.  Group nearest neighbor queries , 2004, Proceedings. 20th International Conference on Data Engineering.

[23]  Christian S. Jensen,et al.  A framework for efficient spatial web object retrieval , 2012, The VLDB Journal.

[24]  Philip E Ross.  Top 11 technologies of the decade , 2011, IEEE Spectrum.

[25]  Xing Xie,et al.  Hybrid index structures for location-based web search , 2005, CIKM '05.

[26]  Fabrizio Grandoni,et al.  A measure & conquer approach for the analysis of exact algorithms , 2009, JACM.

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

[28]  Theodoros Lappas,et al.  Finding a team of experts in social networks , 2009, KDD.

[29]  Bernd-Uwe Pagel,et al.  Towards an analysis of range query performance in spatial data structures , 1993, PODS '93.

[30]  Jianliang Xu,et al.  Spatial-aware interest group queries in location-based social networks , 2014, Data Knowl. Eng..

[31]  Cyrus Shahabi,et al.  Voronoi-Based K Nearest Neighbor Search for Spatial Network Databases , 2004, VLDB.

[32]  D. Hochbaum.  Approximating covering and packing problems: set cover, vertex cover, independent set, and related problems , 1996 .