Mohamed F. Mokbel
Walid G. Aref
ABSTRACT
The emergence of location-aware services calls for new real time spatio-temporal query processing algorithms that deal with large numbers of mobile objects and queries. Online query response is an important characterization of location-aware services. A delay in the answer to a query gives invalid and obsolete results, simply because moving objects can change their locations before the query responds. To handle large numbers of spatio-temporal queries efficiently, we propose the idea of sharing as a means to achieve scalability. In this paper, we introduce several types of sharing in the context of continuous spatio-temporal queries. Examples of sharing in the context of real-time spatio-temporal database systems include sharing the execution, sharing the underlying space, sharing the sliding time windows, and sharing the objects of interest. We demonstrate how sharing can be integrated into query predicates, e.g., selection and spatial join processing. The goal of this paper is to outline research directions and approaches that will lead to scalable and efficient location-aware services.
- S. Acharya,.J. Franklin,and S. B. Zdonik. Disseminating Updates on Broadcast Disks.In VLDB pages 354--365,Bombay,India, Sept. 1996.]] Google Scholar
Digital Library
- W. G. Aref, S. E. Hambrusch, and S. Prabhakar. Pervasive Location Aware Computing Environments (PLACE)http://www.cs.purdue.edu/place/.]]Google Scholar
- S. Babu and J. Widom. Continuous Queries over Data Streams. SIGMOD Record 30(3):109--120,2001.]] Google ScholarDigital Library
- R. Benetis, C. S. Jensen, G. Karciauskas, and S. Saltenis. Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects. In Proceedings of the International Database Engineering and Applications Symposium, IDEAS pages 44--53, Alberta, Canada, July 2002.]] Google ScholarDigital Library
- V. P. Chakka, A. Everspaugh, and J. M. Patel. Indexing Large Trajectory Data Sets with SETI. In Proc. of the Conf. on Innovative Data Systems Research, CIDR Asilomar, CA, Jan. 2003.]]Google Scholar
- U. S. Chakravarthy and J. Minker. Multiple Query Processing in Deductive Databases using Query Graphs. In VLDB pages 384--391, Kyoto, Japan, 1986.]] Google Scholar
- S. Chandrasekaran and M. J. Franklin. Streaming Queries over Streaming Data. In VLDB pages 203--214, Hong Kong, 2002.]]Google Scholar
- J. Chen, D. J. DeWitt, and J. F. Naughton. Design and Evaluation of Alternative Selection Placement Strategies in Optimizing Continuous Queries. In ICDE San Jose, CA, 2002.]]Google Scholar
- J. Chen, D. J. DeWitt, F. Tian, and Y. Wang. NiagaraCQ: A Scalable Continuous Query System for Internet Databases.In SIGMOD pages 379--390,2000.]] Google ScholarDigital Library
- J. Clifford, C. E. Dyreson, T. Isakowitz, C. S. Jensen, and R. T. Snodgrass. On the Semantics of "Now" in Databases. ACM Transactions on Database Systems, TODS 22(2), 1997.]] Google ScholarDigital Library
- C. Faloutsos and S. Roseman. Fractals for Secondary Key Retrieval. In PODS pages 247--252,Philadelphia, PA, ar. 1989.]] Google ScholarDigital Library
- A. Guttman. R-Trees: A Dynamic Index Structure for Spatial Searching. In SIGMOD pages 47--57, Boston, MA, June 1984.]] Google ScholarDigital Library
- S. E. Hambrusch, C.-M. Liu, W. G. Aref, and S. Prabhakar. Query Processing in Broadcasted Spatial Index Trees. In Proceedings of the International Symposium on Advances in Spatial and Temporal Databases, SSTD pages 502--521, Redondo Beach, CA, 2001.]] Google ScholarDigital Library
- M. A. Hammad, M. J. Franklin, W. G. Aref, and A. K. Elmagarmid. Scheduling for shared window joins over data streams. In VLDB Berlin, Germany, Sept. 2003.]]Google ScholarCross Ref
- E. N. Hanson, C. Carnes, L. Huang, M. Konyala, L. Noronha, S. Parthasarathy, J. B. Park, and A. Vernon. Scalable Trigger Processing.In ICDE pages 266--275, Sydney, Austrialia, Mar. 1999.]] Google ScholarDigital Library
- D. Kwon, S. Lee, and S. Lee. Indexing the Current Positions of oving Objects Using the Lazy Update R-tree. In Mobile Data Management, MDM pages 113--120, Jan. 2002.]] Google Scholar
- L. Liu, C. Pu, R. S. Barga, and T. Zhou. Differential Evaluation of Continual Queries.In Proceedings of the International Conference on Distributed Computing Systems, ICDCS pages 458--465,Hong Kong, May 1996.]] Google ScholarDigital Library
- L. Liu, C. Pu, and W. Tang. Continual Queries for Internet Scale Event-Driven Information Delivery.IEEE Transactions on Knowledge and Data Engineering, TKDE 11(4):610--628, 1999.]] Google ScholarDigital Library
- L. Liu, C. Pu, W. Tang, D. Buttler, J. Biggs, T. Zhou, P.Benninghoff.,W. Han, and F. Yu. CQ: A Personalized Update onitoring Toolkit.In SIGMOD pages 547--549, Seattle, WA, June 1998.]] Google ScholarDigital Library
- M.-L. Lo and C. V. Ravishankar. Spatial Hash-Joins. In SIGMOD pages 247--258, Montreal, Canada,1996.]] Google Scholar
- S. Madden, M. Shah, J. M. Hellerstein, and V. Raman. Continuously adaptive continuous queries over streams. In SIGMOD pages 49--60, Madison, Wisconsi, June 2002.]] Google Scholar
- M. F. Mokbel, T. M. Ghanem, and W. G. Aref. Spatio-temporal Access ethods. IEEE Data Engineering Bulletin 26(2):4--49, June 2003.]]Google Scholar
- J. M. Patel and D. J. DeWitt. Partition Based Spatial-erge Join. In SIGMOD pages 259--270, Montreal, Canada, 1996.]] Google Scholar
- D. Pfoser, C. S. Jensen, and Y. Theodoridis. Novel Approaches in Query Processing for oving Object Trajectories.In VLDB pages 395--406,Cairo, Egypt, Sept. 2000.]] Google ScholarDigital Library
- S. Prabhakar, Y. Xia, D. V. Kalashnikov, W. G. Aref, and S. E. Hambrusch. Query Indexing and Velocity Constrained Indexing: Scalable Techniques for Continuous Queries on Moving Objects. IEEE Transactions on Computers 51(10):1124 --1140,2002.]] Google ScholarDigital Library
- P. Roy, S. Seshadri, S. Sudarshan, and S. Bhobe. Efficient and Extensible Algorithms for Multi Query Optimization. In SIGMOD pages 249--260, 2000.]] Google ScholarDigital Library
- S. Saltenis and C. S. Jensen. Indexing of Moving Objects for Location-Based Services. In ICDE SanJose, CA, Feb. 2002.]]Google Scholar
- S. Saltenis, C. S. Jensen, S. T. Leutenegger, and M. A. Lopez. Indexing the Positions of Continuously Moving Objects. In SIGMOD pages 331--342, May 2000.]] Google ScholarDigital Library
- T. K. Sellis. Multiple-Query Optimization. ACM Transactions on Database Systems, TODS 13(1):23--52, 1988.]] Google ScholarDigital Library
- Z. Song and N. Roussopoulos. Hashing Moving Objects. In Mobile Data Management pages 161--172,Hong Kong, Jan.2001.]] Google ScholarDigital Library
- Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In Proceedings of the International Symposium on Advances in Spatial and Temporal Databases, SSTD pages 79--96,Redondo Beach,CA, 2001.]] Google ScholarDigital Library
- Y. Tao and D. Papadias. V3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries. In VLDB pages 431--440,Rome, Italy, Sept. 2001.]] Google ScholarDigital Library
- Y. Tao, D. Papadias, and Q. Shen. Continuous Nearest Neighbor Search. In VLDB pages 287--298,Hong Kong, 2002.]]Google Scholar
- Y. Tao, D. Papadias, and J. Sun. The TPR*-Tree: An Optimized Spatio-temporal Access Method for Predictive Queries. In VLDB Berlin, Germany, Sept. 2003.]]Google Scholar
- J. Tayeb, Ö.Ulusoy, and O. Wolfson. A Quadtree-Based Dynamic Attribute Indexing Method. The Computer Journal 41(3):185--200, 1998.]]Google Scholar
- D. B. Terry, D. Goldberg, D. Nichols, and B. M. Oki. Continuous Queries over Append-Only Databases. In SIGMOD pages 321--330, San Diego, CA, 1992.]] Google Scholar
- Y. Xia and S. Prabhakar. Q+Rtree: Efficient Indexing for oving Object Database. In Proceedings of the International Conference on Database Systems for Advanced Applications, DASFAA pages 175--182,Kyoto, Japan, Mar. 2003.]] Google ScholarDigital Library
Index Terms
- Towards scalable location-aware services: requirements and research issues
Recommendations
Fast Nearest-Neighbor Query Processing in Moving-Object Databases
A desirable feature in spatio-temporal databases is the ability to answer future queries, based on the current data characteristics (reference position and velocity vector). Given a moving query and a set of moving objects, a future query asks for the ...
Moving GeoPQL: a pictorial language towards spatio-temporal queries
Nowadays, two of the main challenges involving spatio-temporal databases concern the integration of their spatial and temporal features to store and query spatial objects changing over time, and the development of a simple and friendly language to query ...
Continuous K-Nearest Neighbor Query for Moving Objects with Uncertain Velocity
AbstractOne of the most important queries in spatio-temporal databases that aim at managing moving objects efficiently is the continuous K-nearest neighbor (CKNN) query. A CKNN query is to retrieve the K-nearest neighbors (KNNs) of a moving user at each ...
Comments