Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Practical selectivity estimation through adaptive sampling
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Sequential sampling procedures for query size estimation
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Query size estimation by adaptive sampling
Selected papers of the 9th annual ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Histogram-based estimation techniques in database systems
Histogram-based estimation techniques in database systems
Efficient mid-query re-optimization of sub-optimal query execution plans
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Approximate medians and other quantiles in one pass and with limited memory
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Selectivity estimation of Window queries for line segment datasets
Proceedings of the seventh international conference on Information and knowledge management
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Spatial join selectivity using power laws
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Selectivity estimation for spatio-temporal queries to moving objects
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
A Framework for Generating Network-Based Moving Objects
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SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
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Selectivity Estimation for Spatial Joins with Geometric Selections
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
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ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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Proceedings of the 17th International Conference on Data Engineering
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VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Fast Incremental Maintenance of Approximate Histograms
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
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Fast range query estimation by N-level tree histograms
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ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
PLACE: a query processor for handling real-time spatio-temporal data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Challenges in spatiotemporal stream query optimization
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Parametric model for video content analysis
Pattern Recognition Letters
Query optimization for spatio-temporal data stream management systems
SIGSPATIAL Special
Spatial Selectivity Estimation Using Cumulative Density Wavelet Histogram
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Estimating Selectivity for Current Query of Moving Objects Using Index-Based Histogram
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Continuous aggregate nearest neighbor queries
Geoinformatica
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This paper presents a framework for building and continuously maintaining spatio-temporal histograms (ST-Histograms, for short). ST-Histograms are used for selectivity estimation of continuous pipelined query operators. Unlike traditional histograms that examine and/or sample all incoming data tuples, ST-Histograms are built by monitoring the actual selectivities of the outstanding continuous queries. ST-Histograms have three main features: (1) The ST-Histograms are built with (almost) no overhead to the system. We use only feedback (i.e., the actual selectivity) from the existing continuous queries. (2) Rather than wasting system resources in maintaining accurate histograms for the whole spatial space, we only maintain accurate histograms for that part of the space that is relevant to the current existing queries. The rest of the space has less accurate histograms. (3) The ST-Histograms are equipped with a periodicity detection procedure that predicts the future execution of the continuous queries. Hence, the query processing engine can continuously adapt the continuous query pipeline to reflect this prediction. Experimental results based on a real implementation inside a data stream management system show a superior performance of ST-Histograms in terms of providing accurate operator selectivity estimations with no extra overhead.