SCUBA: Scalable Cluster-Based Algorithm for Evaluating Continuous Spatio-temporal Queries on Moving Objects
Conference paper
Abstract
In this paper, we propose, SCUBA, a Scalable Cl uster Based Algorithm for evaluating a large set of continuous queries over spatio-temporal data streams. The key idea of SCUBA is to group moving objects and queries based on common spatio-temporal properties at run-time into moving clusters to optimize query execution and thus facilitate scalability. SCUBA exploits shared cluster-based execution by abstracting the evaluation of a set of spatio-temporal queries as a spatial join first between moving clusters. This cluster-based filtering prunes true negatives. Then the execution proceeds with a fine-grained within-moving-cluster join process for all pairs of moving clusters identified as potentially joinable by a positive cluster-join match. A moving cluster can serve as an approximation of the location of its members. We show how moving clusters can serve as means for intelligent load shedding of spatio-temporal data to avoid performance degradation with minimal harm to result quality. Our experiments on real datasets demonstrate that SCUBA can achieve a substantial improvement when executing continuous queries on spatio-temporal data streams.
Keywords
Cluster Member Query Execution Continuous Query Location Update Incremental Cluster
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
- 1.Agrawal, R., et al.: Automatic subspace clustering of high dimensional data for data mining applications. In: SIGMOD, pp. 94–105 (1998)Google Scholar
- 2.Babcock, B., Datar, M., Motwani, R.: Load shedding techniques for data stream systems (2003)Google Scholar
- 3.Barbará, D.: Chaotic mining: Knowledge discovery using the fractal dimension. In: SIGMOD Workshop on Data Mining and Knowl. Discovery (1999)Google Scholar
- 4.Barbará, D.: Requirements for clustering data streams. SIGKDD Explorations 3(2), 23–27 (2002)CrossRefGoogle Scholar
- 5.Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)zbMATHCrossRefGoogle Scholar
- 6.Compton, C.L., Tennenhouse, D.L.: Collaborative load shedding for mediabased applications. In: Int. Conf. on Multimedia Computing and Systems (1994)Google Scholar
- 7.Domingos, P., Hulten, G.: Catching up with the data: Research issues in mining data streams. In: DMKD (2001)Google Scholar
- 8.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience Publication, Hoboken (2000)Google Scholar
- 9.Elmongui, H.G., Mokbel, M.F., Aref, W.G.: Spatio-temporal histograms. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 19–36. Springer, Heidelberg (2005)CrossRefGoogle Scholar
- 10.Fisher, D.H.: Iterative optimization and simplification of hierarchical clusterings. CoRR, cs.AI/9604103 (1996)Google Scholar
- 11.Gedik, B., Liu, L.: Distributed processing of continuously moving queries on moving objects in a mobile system. In: EDBT 2002, pp. 67–87 (2004)Google Scholar
- 12.Guha, S., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams. In: FOCS, pp. 359–366 (2000)Google Scholar
- 13.Gupta, S.K., Rao, K.S., Bhatnagar, V.: K-means clustering algorithm for categorical attributes. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 203–208. Springer, Heidelberg (1999)Google Scholar
- 14.Hambrusch, S.E., Liu, C.-M., Aref, W.G., Prabhakar, S.: Query processing in broadcasted spatial index trees. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, p. 502. Springer, Heidelberg (2001)CrossRefGoogle Scholar
- 15.Har-Peled, S.: Clustering motion. In: FOCS 2001: Proceedings of the 42nd IEEE symposium on Foundations of Computer Science, p. 84 (2001)Google Scholar
- 16.Hartigan, J.A.: Clustering Algorithms. John Wiley and Sons, Chichester (1975)zbMATHGoogle Scholar
- 17.Hu, H., Xu, J., Lee, D.L.: A generic framework for monitoring continuous spatial queries over moving objects. In: SIGMOD (2005)Google Scholar
- 18.Jacobson, V.: Congestion avoidance and control. SIGCOMM Comput. Commun.Rev. 25(1), 157–187 (1995)CrossRefGoogle Scholar
- 19.Jain, A.K., Murthy, M.N., Flynn, P.J.: Data clustering: A review. Technical Report MSU-CSE-00-16, Dept. of CS, Michigan State University (2000)Google Scholar
- 20.Kalashnikov, D.V., et al.: Main memory evaluation of monitoring queries over moving objects. Distrib. Parallel Databases 15(2) (2004)Google Scholar
- 21.Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatiotemporal data. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 364–381. Springer, Heidelberg (2005)CrossRefGoogle Scholar
- 22.Li, Y., Han, J., Yang, J.: Clustering moving objects. In: KDD, pp. 617–622 (2004)Google Scholar
- 23.Mokbel, M.F., et al.: Towards scalable location-aware services: requirements and research issues. In: GIS, pp. 110–117 (2003)Google Scholar
- 24.Mokbel, M.F., Xiong, X., Aref, W.G.: Sina: Scalable incremental processing of continuous queries in spatio-temporal databases. In: SIGMOD, pp. 623–634 (2004)Google Scholar
- 25.Ng, R.T., Han, J.: Efficient and effective clustering methods for spatial data mining. In: VLDB, pp. 144–155 (1994)Google Scholar
- 26.O’Callaghan, L., et al.: Streaming-data algorithms for high-quality clustering. In: ICDE, p. 685 (2002)Google Scholar
- 27.Papadias, D., et al.: Conceptual partitioning: An efficient method for continuous nearest neighbor monitoring. In: SIGMOD (2005)Google Scholar
- 28.Philip, Y.C.: Loadstar: A load shedding scheme for classifying data streamsGoogle Scholar
- 29.Prabhakar, S., et al.: Query indexing and velocity constrained indexing: Scalable techniques for continuous queries on moving objects. IEEE Trans. Computers 51(10) (2002)Google Scholar
- 30.Rasmussen, E.M.: Clustering algorithms. In: Information Retrieval: Data Structures & Algorithms, pp. 419–442 (1992)Google Scholar
- 31.Rundensteiner, E.A., Ding, L., et al.: Cape: Continuous query engine with heterogeneous-grained adaptivity. In: VLDB, pp. 1353–1356 (2004)Google Scholar
- 32.Saltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: SIGMOD, pp. 331–342 (2000)Google Scholar
- 33.Sistla, A.P., Wolfson, O., Chamberlain, S., Dao, S.: Modeling and querying moving objects. In: ICDE, pp. 422–432 (1997)Google Scholar
- 34.Tao, Y., Papadias, D.: Time-parameterized queries in spatio-temporal databases. In: SIGMOD, pp. 334–345 (2002)Google Scholar
- 35.Tatbul, N.: QoS-driven load shedding on data streams. In: Chaudhri, A.B., Unland, R., Djeraba, C., Lindner, W. (eds.) EDBT 2002. LNCS, vol. 2490, pp. 566–576. Springer, Heidelberg (2002)CrossRefGoogle Scholar
- 36.Tatbul, N., Çetintemel, U., Zdonik, S.B., Cherniack, M., Stonebraker, M.: Load shedding in a data stream manager. In: VLDB, pp. 309–320 (2003)Google Scholar
- 37.Tayeb, J., Ulusoy, Ö., Wolfson, O.: A quadtree-based dynamic attribute indexing method. Comput. J. 41(3), 185–200 (1998)zbMATHCrossRefGoogle Scholar
- 38.Xiong, X., Mokbel, M.F., et al.: Scalable spatio-temporal continuous query processing for location-aware services. In: SSDBM, p. 317 (2004)Google Scholar
- 39.Xiong, X., Mokbel, M.F., Aref, W.G.: Sea-cnn: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In: ICDE, pp. 643–654 (2005)Google Scholar
- 40.Ye, N., Li, X.: A scalable, incremental learning algorithm for classification problems. Comput. Ind. Eng. 43(4), 677–692 (2002)CrossRefMathSciNetGoogle Scholar
- 41.Zhang, T., Ramakrishnan, R., Livny, M.: Birch: An efficient data clustering method for very large databases. In: SIGMOD, pp. 103–114 (1996)Google Scholar
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