Abstract
In many applications, such as road traffic supervision and location based mobile service in large cities, moving objects continue to generate large amount of spatio-temporal information in the form of data streams. How to get qualified answers for aggregate queries appears to be a big challenge due to the high dynamic nature of data streams. Previous methods (e.g., AMH[11]) mainly focus on efficient organization of spatio-temporal information and rapid response time, not the quality of the answer. Our main contribution is a novel method to process important aggregate queries (e.g. SUM and AVG) based on a new structure (named AMH*) to summarize spatio-temporal information. The analysis in theory shows that the relative error and (/or) absolute error of answers can be ensured smaller than predefined parameters. A series of extended experiments evaluate the correctness of our approach.
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References
Aboulnaga, A., Chaudhuri, S.: Self-tuning histograms: Building histograms without looking at data. In: Proc. of ACM SIGMOD, ACM Press, New York (1999)
Beckmann, N., Kriegel, H., Schneider, R., Seeger, B.: The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. In: Proc. of ACM SIGMOD, ACM Press, New York (1990)
Bruno, N., Chaudhuri, S., Gravano, L.: Stholes: A multidimensional workload-aware histogram. In: Proc. of ACM SIGMOD, ACM Press, New York (2001)
Jin, C., Xiong, F., Huang, J.Z., Yu, J.X., Zhou, A.: Mining Frequent Items in Spatio-temporal Databases. In: Li, Q., Wang, G., Feng, L. (eds.) WAIM 2004. LNCS, vol. 3129, pp. 549–558. Springer, Heidelberg (2004)
Lee, J., Kim, D., Chung, C.: Multi-dimensional selectivity estimation using compressed histogram information. In: Proc. of ACM SIGMOD, ACM Press, New York (1999)
Lopez, I.F.V., Snodgrass, R.T., Moon, B.: Spatiotemporal aggregate computation: A survey. IEEE Transactions on Knowledge and Data Engineering 17(2) (2005)
Matias, Y., Vitter, J., Wang, M.: Wavelet-based histograms for selectivity estimation. In: Proc. of ACM SIGMOD, ACM Press, New York (1998)
Papadias, D., Tao, Y., Kalnis, P., Zhang, J.: Indexing spatio-temporal data warehouses. In: Proc. of ICDE (2002)
Pelanis, M., Šaltenis, S., Jensen, C.S.: Indexing the past, present, and anticipated future positions of moving objects. ACM Transactions on Database Systems 31(1) (2006)
Saltenis, S., Jensen, C., Leutenegger, S., Lopez, M.: Indexing the positions of continuously moving objects. In: Proc. of SIGMOD (2000)
Sun, J., Papadias, D., Tao, Y., Liu, B.: Querying about the past, the present, and the future in spatio-temporal databases. In: Proc. of ICDE (2004)
Zhang, D., Tsotras, V., Gunopulos, D.: Efficient aggregation over objects with extents. In: Proc. of PODS (2002)
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Jin, C., Guo, W., Zhao, F. (2007). Getting Qualified Answers for Aggregate Queries in Spatio-temporal Databases. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_25
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DOI: https://doi.org/10.1007/978-3-540-72524-4_25
Publisher Name: Springer, Berlin, Heidelberg
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