Spatio-temporal aggregation using sketches

Several spatio-temporal applications require the retrieval of summarized information about moving objects that lie in a query region during a query interval (e.g., the number of mobile users covered by a cell, traffic volume in a district, etc.). Existing solutions have the distinct counting problem: if an object remains in the query region for several timestamps during the query interval, it will be counted multiple times in the result. We solve this problem by integrating spatio-temporal indexes with sketches, traditionally used for approximate query processing. The proposed techniques can also be applied to reduce the space requirements of conventional spatio-temporal data and to mine spatio-temporal association rules.

[1]  Christos Faloutsos,et al.  The R+-Tree: A Dynamic Index for Multi-Dimensional Objects , 1987, VLDB.

[2]  Jimeng Sun,et al.  Analysis of predictive spatio-temporal queries , 2003, TODS.

[3]  Rajeev Rastogi,et al.  Processing set expressions over continuous update streams , 2003, SIGMOD '03.

[4]  Panos Kalnis,et al.  Efficient OLAP Operations in Spatial Data Warehouses , 2001, SSTD.

[5]  Rajeev Motwani,et al.  Overcoming limitations of sampling for aggregation queries , 2001, Proceedings 17th International Conference on Data Engineering.

[6]  Jimeng Sun,et al.  The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries , 2003, VLDB.

[7]  Pankaj K. Agarwal,et al.  CRB-Tree: An Efficient Indexing Scheme for Range-Aggregate Queries , 2003, ICDT.

[8]  SeegerBernhard,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990 .

[9]  Helen J. Wang,et al.  Online aggregation , 1997, SIGMOD '97.

[10]  RoussopoulosNick,et al.  Adaptive selectivity estimation using query feedback , 1994 .

[11]  Dimitrios Gunopulos,et al.  Efficient aggregation over objects with extent , 2002, PODS '02.

[12]  S JensenChristian,et al.  Indexing the positions of continuously moving objects , 2000 .

[13]  Yufei Tao,et al.  Aggregate Processing of Planar Points , 2002, EDBT.

[14]  Forouzan Golshani,et al.  Proceedings of the Eighth International Conference on Data Engineering , 1992 .

[15]  Panos Kalnis,et al.  Indexing spatio-temporal data warehouses , 2002, Proceedings 18th International Conference on Data Engineering.

[16]  Philippe Flajolet,et al.  Probabilistic Counting Algorithms for Data Base Applications , 1985, J. Comput. Syst. Sci..

[17]  Nick Roussopoulos,et al.  Faloutsos: "the r+- tree: a dynamic index for multidimensional objects , 1987 .

[18]  Sharad Mehrotra,et al.  Progressive approximate aggregate queries with a multi-resolution tree structure , 2001, SIGMOD '01.

[19]  Sudipto Guha,et al.  Dynamic multidimensional histograms , 2002, SIGMOD '02.

[20]  Christian S. Jensen,et al.  Lopez: "Indexing the Positions of Continuously Moving Objects , 2000, SIGMOD 2000.

[21]  Nick Roussopoulos,et al.  Adaptive selectivity estimation using query feedback , 1994, SIGMOD '94.

[22]  Christos Faloutsos,et al.  ANF: a fast and scalable tool for data mining in massive graphs , 2002, KDD.

[23]  Christian S. Jensen,et al.  Indexing the Positions of Continuously Moving Objects , 2000, SIGMOD Conference.

[24]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

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

[26]  Jeffrey Considine,et al.  Approximate aggregation techniques for sensor databases , 2004, Proceedings. 20th International Conference on Data Engineering.

[27]  Hans-Joachim Lenz,et al.  PISA: Performance models for Index Structures with and without Aggregated data , 1999, Proceedings. Eleventh International Conference on Scientific and Statistical Database Management.

[28]  Dimitrios Gunopulos,et al.  Efficient computation of temporal aggregates with range predicates , 2001, PODS '01.