Multi-source Skyline Query Processing in Road Networks

Skyline query processing has been investigated extensively in recent years, mostly for only one query reference point. An example of a single-source skyline query is to find hotels which are cheap and close to the beach (an absolute query), or close to a user-given location (a relatively query). A multi-source skyline query considers several query points at the same time (e.g., to find hotels which are cheap and close to the University, the Botanic Garden and the China Town). In this paper, we consider the problem of efficient multi-source skyline query processing in road networks. It is not only the first effort to consider multi-source skyline query in road networks but also the first effort to process the relative skyline queries where the network distance between two locations needs to be computed on-the-fly. Three different query processing algorithms are proposed and evaluated in this paper. The Lower Bound Constraint algorithm (LBC) is proven to be an instance optimal algorithm. Extensive experiments using large real road network datasets demonstrate that LBC is four times more efficient than a straightforward algorithm.

[1]  Victor C. S. Lee,et al.  Distance indexing on road networks , 2006, VLDB.

[2]  H. V. Jagadish,et al.  Algorithms for Searching Massive Graphs , 1994, IEEE Trans. Knowl. Data Eng..

[3]  Donald Kossmann,et al.  Shooting Stars in the Sky: An Online Algorithm for Skyline Queries , 2002, VLDB.

[4]  Mihalis Yannakakis,et al.  Multiobjective query optimization , 2001, PODS '01.

[5]  Qing Liu,et al.  A multi-resolution surface distance model for k-NN query processing , 2008, The VLDB Journal.

[6]  Moni Naor,et al.  Optimal aggregation algorithms for middleware , 2001, PODS.

[7]  Jan Chomicki,et al.  Skyline with presorting , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[8]  Raghu Ramakrishnan,et al.  A performance study of transitive closure algorithms , 1994, SIGMOD '94.

[9]  Sakti Pramanik,et al.  An Efficient Path Computation Model for Hierarchically Structured Topographical Road Maps , 2002, IEEE Trans. Knowl. Data Eng..

[10]  Elke A. Rundensteiner,et al.  Hierarchical Encoded Path Views for Path Query Processing: An Optimal Model and Its Performance Evaluation , 1998, IEEE Trans. Knowl. Data Eng..

[11]  Cyrus Shahabi,et al.  The spatial skyline queries , 2006, VLDB.

[12]  Hongjun Lu,et al.  Stabbing the sky: efficient skyline computation over sliding windows , 2005, 21st International Conference on Data Engineering (ICDE'05).

[13]  Dimitris Papadias,et al.  Aggregate nearest neighbor queries in road networks , 2005, IEEE Transactions on Knowledge and Data Engineering.

[14]  H. V. Jagadish,et al.  Direct transitive closure algorithms: design and performance evaluation , 1990, TODS.

[15]  Donald Kossmann,et al.  The Skyline operator , 2001, Proceedings 17th International Conference on Data Engineering.

[16]  Elke A. Rundensteiner,et al.  Hierarchical Path Views: A Model Based on Fragmentation and Transportation Road Types , 1995, ACM-GIS.

[17]  Bernhard Seeger,et al.  An optimal and progressive algorithm for skyline queries , 2003, SIGMOD '03.

[18]  Beng Chin Ooi,et al.  Efficient Progressive Skyline Computation , 2001, VLDB.

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

[20]  Yufei Tao,et al.  Query Processing in Spatial Network Databases , 2003, VLDB.

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

[22]  Roy Goldman,et al.  Proximity Search in Databases , 1998, VLDB.

[23]  Michael Stonebraker,et al.  Heuristic Search in Data Base Systems , 1984, Expert Database Workshop.

[24]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[25]  Wolf-Tilo Balke,et al.  Multi-objective Query Processing for Database Systems , 2004, VLDB.

[26]  Heng Tao Shen,et al.  Surface k-NN Query Processing , 2006, 22nd International Conference on Data Engineering (ICDE'06).