Optimization of Spatial Joins on Mobile Devices

Mobile devices like PDAs are capable of retrieving information from various types of services. In many cases, the user requests cannot directly be processed by the service providers, if their hosts have limited query capabilities or the query combines data from various sources, which do not collaborate with each other. In this paper, we present a framework for optimizing spatial join queries that belong to this class. We presume that the connection and queries are ad-hoc, there is no mediator available and the services are non-collaborative. We also assume that the services are not willing to share their statistics or indexes with the client. We retrieve statistics dynamically in order to generate a low-cost execution plan, while considering the storage and computational power limitations of the PDA. Since acquiring the statistics causes overhead, we describe an adaptive algorithm that optimizes the overall process of statistics retrieval and query execution. We demonstrate the applicability of our methods with a prototype implementation on a PDA with wireless network access.

[1]  Dimitris Papadias,et al.  Integration of spatial join algorithms for processing multiple inputs , 1999, SIGMOD '99.

[2]  Beng Chin Ooi,et al.  Exploiting Spatial Indexes for Semijoin-Based Join Processing in Distributed Spatial Databases , 2000, IEEE Trans. Knowl. Data Eng..

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

[4]  Ming-Ling Lo,et al.  Spatial hash-joins , 1996, SIGMOD '96.

[5]  Laura M. Haas,et al.  Cost Models DO Matter: Providing Cost Information for Diverse Data Sources in a Federated System , 1999, VLDB.

[6]  Sridhar Ramaswamy,et al.  Scalable Sweeping-Based Spatial Join , 1998, VLDB.

[7]  Yannis Manolopoulos,et al.  Closest pair queries in spatial databases , 2000, SIGMOD '00.

[8]  Alon Y. Halevy,et al.  An adaptive query execution system for data integration , 1999, SIGMOD '99.

[9]  Patrick Valduriez,et al.  Scaling Access to Heterogeneous Data Sources with DISCO , 1998, IEEE Trans. Knowl. Data Eng..

[10]  Sukho Lee,et al.  Adaptive multi-stage distance join processing , 2000, SIGMOD '00.

[11]  Ee-Peng Lim,et al.  Efficient k nearest neighbor queries on remote spatial databases using range estimation , 2002, Proceedings 14th International Conference on Scientific and Statistical Database Management.

[12]  ManolopoulosYannis,et al.  Closest pair queries in spatial databases , 2000 .

[13]  Hans-Peter Kriegel,et al.  Efficient processing of spatial joins using R-trees , 1993, SIGMOD Conference.

[14]  Jeffrey F. Naughton,et al.  A non-blocking parallel spatial join algorithm , 2002, Proceedings 18th International Conference on Data Engineering.

[15]  Bernhard Seeger,et al.  Data redundancy and duplicate detection in spatial join processing , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

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

[17]  K. Selçuk Candan,et al.  Query caching and optimization in distributed mediator systems , 1996, SIGMOD '96.

[18]  Yufei Tao,et al.  All-nearest-neighbors queries in spatial databases , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..

[19]  David J. DeWitt,et al.  Partition based spatial-merge join , 1996, SIGMOD '96.