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Improved histograms for selectivity estimation of range predicates

Viswanath Poosala, Peter J. Haas, Yannis E. Ioannidis, Eugene J. Shekita
1996 SIGMOD record  
Finally, we present results from an empirtcal study of the proposed histogram types used in selectivity estimation of range predicates and identify the histogram types that have the best overall performance  ...  Although several types of histograms have been proposed in the past, there has never been a systematic study of all histogram aspects,the available choices for each aspect, and the impact of such choices  ...  Suppose we wish to estimate the result size for the range predicate 10 s X <25.  ... 
doi:10.1145/235968.233342 fatcat:jrtynzealja3hp6zm7l5ojwhaa

Improved histograms for selectivity estimation of range predicates

Viswanath Poosala, Peter J. Haas, Yannis E. Ioannidis, Eugene J. Shekita
1996 Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96  
Finally, we present results from an empirtcal study of the proposed histogram types used in selectivity estimation of range predicates and identify the histogram types that have the best overall performance  ...  Although several types of histograms have been proposed in the past, there has never been a systematic study of all histogram aspects,the available choices for each aspect, and the impact of such choices  ...  Suppose we wish to estimate the result size for the range predicate 10 s X <25.  ... 
doi:10.1145/233269.233342 dblp:conf/sigmod/PoosalaIHS96 fatcat:zl62uk2vfbcxthqdzh7ct5lmee

Selectivity estimation for range predicates using lightweight models

Anshuman Dutt, Chi Wang, Azade Nazi, Srikanth Kandula, Vivek Narasayya, Surajit Chaudhuri
2019 Proceedings of the VLDB Endowment  
We explore application of neural networks and tree-based ensembles to the important problem of selectivity estimation of multi-dimensional range predicates.  ...  Query optimizers depend on selectivity estimates of query predicates to produce a good execution plan.  ...  selectivity estimates for conjunction of predicates.  ... 
doi:10.14778/3329772.3329780 fatcat:tfd3rj5zcfavpnxq2wxqu4akmq

Estimating the Selectivity of XML Path Expression with Predicates by Histograms [chapter]

Yu Wang, Haixun Wang, Xiaofeng Meng, Shan Wang
2004 Lecture Notes in Computer Science  
In this paper, we propose a novel method based on 2-dimensional value histograms to estimate the selectivity of path expressions embedded with predicates.  ...  A path expression may contain multiple branches with predicates, each of which having its impact on the selectivity of the entire query.  ...  Next, we show how the histograms can be used for selectivity estimation. Basic Histogram Operations We use histograms for selectivity estimation of path expressions with predicates.  ... 
doi:10.1007/978-3-540-27772-9_41 fatcat:f63zcnf52fba5kesziu6ku7jqq

Reducing the Footprint of Main Memory HTAP Systems: Removing, Compressing, Tiering, and Ignoring Data

Martin Boissier
2018 Very Large Data Bases Conference  
Since advantages of a reduced footprint are manyfold, the issue is of high importance for main memory-resident databases.  ...  First, we reduce existing allocations by efficiently selecting indices and workload-driven compression configurations for table data.  ...  Each value range further stores the number of elements within the range and the number of distinct elements. Multi-Column Pruning Histograms are each created between two selected attributes.  ... 
dblp:conf/vldb/Boissier18 fatcat:ebzea5pvjrg4zlgwk5eykdajki

QuickSel: Quick Selectivity Learning with Mixture Models [article]

Yongjoo Park, Shucheng Zhong, Barzan Mozafari
2018 arXiv   pre-print
Estimating the selectivity of a query is a key step in almost any cost-based query optimizer.  ...  As an alternative to scans, query-driven histograms have been proposed, which refine the histograms based on the actual selectivities of the observed queries.  ...  number of tuples in T [l i , u i ] the range of the values in C i x a tuple of T B 0 the domain of x; [l 1 , u 1 ] × · · · × [l d , u d ] P i i-th predicate B i hyperrectangle range for the i-th predicate  ... 
arXiv:1812.10568v1 fatcat:4rzhgjby3rcxrbollvy6zwjrue

Exploiting statistics on query expressions for optimization

Nicolas Bruno, Surajit Chaudhuri
2002 Proceedings of the 2002 ACM SIGMOD international conference on Management of data - SIGMOD '02  
This approach can introduce large estimation errors, which may result in the optimizer choosing inefficient execution plans.  ...  Finally, we present experimental results on an implementation of our approach in Microsoft SQL Server 2000.  ...  ACKNOWLEDGEMENTS We thank Luis Gravano, Christian König, and Vivek Narasayya for their valuable feedback.  ... 
doi:10.1145/564720.564722 fatcat:cvw6vyavzzcdlha3a3kpiqnbzq

Exploiting statistics on query expressions for optimization

Nicolas Bruno, Surajit Chaudhuri
2002 Proceedings of the 2002 ACM SIGMOD international conference on Management of data - SIGMOD '02  
Finally, we present experimental results on an implementation of our approach in Microsoft SQL Server 2000. Cost Estimation Module Enumeration Engine (e.g., rules)  ...  This approach can introduce large estimation errors, which may result in the optimizer choosing inefficient execution plans.  ...  ACKNOWLEDGEMENTS We thank Luis Gravano, Christian König, and Vivek Narasayya for their valuable feedback.  ... 
doi:10.1145/564691.564722 dblp:conf/sigmod/BrunoC02 fatcat:nuvhrt62brdwzabgpa6nx6ld7u

Query Selectivity Estimation via Data Mining [chapter]

Jarek Gryz, Dongming Liang
2004 Intelligent Information Processing and Web Mining  
We present experimental results indicating that empty joins are common in real data sets and propose a simple strategy that uses information about empty joins for an improved join selectivity estimation  ...  We propose a new approach to join selectivity estimation.  ...  The optimizer can then use the new predicate to get a better estimate of the query result cardinality.  ... 
doi:10.1007/978-3-540-39985-8_4 fatcat:7ykywnvc4vhodgdejbvzpk4br4

Approximate Query Processing: Taming the TeraBytes

Minos N. Garofalakis, Phillip B. Gibbons
2001 Very Large Data Bases Conference  
is subset of sample that satisfies the predicate • Quality of the estimate depends only on the variance in R & |S| after the predicate: So 10K sample may suffice for 10B row relation!  ...  Good choice for Pj's results in tighter confidence intervals decomposition: [2.75, -1.25, 0.5, 0, 0, -1, -1, 0] 26 Garofalakis & Gibbons, VLDB 2001 # range-query selectivity estimation • Key idea: use  ...  -Studies the effectiveness of histograms, kernel-density estimators, and their hybrids for estimating the selectivity of range queries over metric attributes with large domains. • -Precursor to [CDN01]  ... 
dblp:conf/vldb/GarofalakisG01 fatcat:ckt4oz7y25f2jmzm5af3jbwax4

Selectivity Estimation for Fuzzy String Predicates in Large Data Sets

Liang Jin, Chen Li
2005 Very Large Data Bases Conference  
In this paper, we study the problem of estimating selectivities of fuzzy string predicates. We develop a novel technique, called Sepia, to solve the problem.  ...  Our extensive experiments on real data sets show that this technique can accurately estimate selectivities of fuzzy string predicates.  ...  Thus a histogram based on such an ordering does not provide accurate selectivity estimation for a fuzzy string predicate.  ... 
dblp:conf/vldb/JinL05 fatcat:eu75lvc3ybbm5ojtkcn44gif4e

Searching on the Secondary Structure of Protein Sequences [chapter]

Laurie Hammel, Jignesh M. Patel
2002 VLDB '02: Proceedings of the 28th International Conference on Very Large Databases  
As part of the Periscope implementation we have also developed a framework for optimizing these queries and for accurately estimating the costs of the various query evaluation plans.  ...  In spite of the many decades of progress in database research, surprisingly scientists in the life sciences community still struggle with inefficient and awkward tools for querying biological data sets  ...  We would like to thank Jack Dixon, Don Huddler, and Jeanne Stuckey at the Univ. of Michigan's Dept. of Biological Chemistry, and Ernst Dow of Eli Lilly, for their helpful discussions about the data analysis  ... 
doi:10.1016/b978-155860869-6/50062-7 dblp:conf/vldb/HammelP02 fatcat:4uczr5q7kbcerl7dz5ndc7qvjm

Query optimization for spatio-temporal data stream management systems

Hicham G. Elmongui
2009 SIGSPATIAL Special  
In this paper, we focus on the optimization of multi-predicate spatio-temporal queries on moving objects. In particular, we provide a costing mechanism for continuous spatio-temporal queries.  ...  We provide for the optimization of the parameters of the spatiotemporal operators.  ...  This cost needs future estimates from ST-Histogram. Since ST-Histogram provides current estimates only, we provide for a mechanism to estimate future selectivities from current ones.  ... 
doi:10.1145/1517463.1517465 fatcat:2l2kpssrajefhdwwfkdqarw6gy

Collecting and Maintaining Just-in-Time Statistics

Amr El-Helw, Ihab F. Ilyas, Wing Lau, Volker Markl, Calisto Zuzarte
2007 2007 IEEE 23rd International Conference on Data Engineering  
Overall, this decoupling often leads to large cardinality estimation errors and, in consequence, to the selection of suboptimal plans for query execution.  ...  The collected statistics are materialized and incrementally updated for later reuse. We present the basic concepts, architecture, and key features of JITS.  ...  We also thank John Hornibrook, Qi Cheng, Ivan Popivanov, Xiaoyan Qian, Matthew Huras and Berni Schiefer for their feedback and comments, which have greatly improved the prototype, as well as Peter Haas  ... 
doi:10.1109/icde.2007.367897 dblp:conf/icde/El-HelwILMZ07 fatcat:6s3xg45k5bhcnpu23hebykaf7y

SEPIA: estimating selectivities of approximate string predicates in large Databases

Liang Jin, Chen Li, Rares Vernica
2008 The VLDB journal  
In this paper, we study the problem of estimating selectivities of fuzzy string predicates. We develop a novel technique, called Sepia, to solve the problem.  ...  Our extensive experiments on real data sets show that this technique can accurately estimate selectivities of fuzzy string predicates.  ...  Thus a histogram based on such an ordering does not provide accurate selectivity estimation for a fuzzy string predicate.  ... 
doi:10.1007/s00778-007-0061-2 fatcat:axxg7w7tkjdbhjkus4vzeu5acu
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