Histograms based on the minimum description length principle

Abstract Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many OLAP, data mining, and data visualization applications. This paper presents a new family of histograms, the Hierarchical Model Fitting (HMF) histograms,...
Ausführliche Beschreibung

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Autor*in:	Wang, Hai [verfasserIn] Sevcik, Kenneth C.

Format:	Artikel
Sprache:	Englisch

Erschienen:	2006

Schlagwörter:	Query processing Approximate query answering Data summarization Histograms

Anmerkung:	© Springer-Verlag 2006

Übergeordnetes Werk:	Enthalten in: The VLDB journal - Springer-Verlag, 1992, 17(2006), 3 vom: 14. Dez., Seite 419-442
Übergeordnetes Werk:	volume:17 ; year:2006 ; number:3 ; day:14 ; month:12 ; pages:419-442

Links:	Volltext

DOI / URN:	10.1007/s00778-006-0015-0

Katalog-ID:	OLC2051357439

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allfieldsGer	10.1007/s00778-006-0015-0 doi (DE-627)OLC2051357439 (DE-He213)s00778-006-0015-0-p DE-627 ger DE-627 rakwb eng 004 VZ Wang, Hai verfasserin aut Histograms based on the minimum description length principle 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2006 Abstract Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many OLAP, data mining, and data visualization applications. This paper presents a new family of histograms, the Hierarchical Model Fitting (HMF) histograms, based on the Minimum Description Length principle. Rather than having each bucket of a histogram described by the same type of model, the HMF histograms employ a local optimal model for each bucket. The improved effectiveness of the locally chosen models offsets more than the overhead of keeping track of the representation of each individual bucket. Through a set of experiments, we show that the HMF histograms are capable of providing more accurate approximations than previously proposed techniques for many real and synthetic data sets across a variety of query workloads. Query processing Approximate query answering Data summarization Histograms Sevcik, Kenneth C. aut Enthalten in The VLDB journal Springer-Verlag, 1992 17(2006), 3 vom: 14. Dez., Seite 419-442 (DE-627)170933059 (DE-600)1129061-4 (DE-576)032856466 1066-8888 nnns volume:17 year:2006 number:3 day:14 month:12 pages:419-442 https://doi.org/10.1007/s00778-006-0015-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_62 GBV_ILN_65 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_4116 GBV_ILN_4126 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4311 AR 17 2006 3 14 12 419-442
allfieldsSound	10.1007/s00778-006-0015-0 doi (DE-627)OLC2051357439 (DE-He213)s00778-006-0015-0-p DE-627 ger DE-627 rakwb eng 004 VZ Wang, Hai verfasserin aut Histograms based on the minimum description length principle 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2006 Abstract Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many OLAP, data mining, and data visualization applications. This paper presents a new family of histograms, the Hierarchical Model Fitting (HMF) histograms, based on the Minimum Description Length principle. Rather than having each bucket of a histogram described by the same type of model, the HMF histograms employ a local optimal model for each bucket. The improved effectiveness of the locally chosen models offsets more than the overhead of keeping track of the representation of each individual bucket. Through a set of experiments, we show that the HMF histograms are capable of providing more accurate approximations than previously proposed techniques for many real and synthetic data sets across a variety of query workloads. Query processing Approximate query answering Data summarization Histograms Sevcik, Kenneth C. aut Enthalten in The VLDB journal Springer-Verlag, 1992 17(2006), 3 vom: 14. Dez., Seite 419-442 (DE-627)170933059 (DE-600)1129061-4 (DE-576)032856466 1066-8888 nnns volume:17 year:2006 number:3 day:14 month:12 pages:419-442 https://doi.org/10.1007/s00778-006-0015-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_62 GBV_ILN_65 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_4116 GBV_ILN_4126 GBV_ILN_4266 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4311 AR 17 2006 3 14 12 419-442
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abstract	Abstract Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many OLAP, data mining, and data visualization applications. This paper presents a new family of histograms, the Hierarchical Model Fitting (HMF) histograms, based on the Minimum Description Length principle. Rather than having each bucket of a histogram described by the same type of model, the HMF histograms employ a local optimal model for each bucket. The improved effectiveness of the locally chosen models offsets more than the overhead of keeping track of the representation of each individual bucket. Through a set of experiments, we show that the HMF histograms are capable of providing more accurate approximations than previously proposed techniques for many real and synthetic data sets across a variety of query workloads. © Springer-Verlag 2006
abstractGer	Abstract Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many OLAP, data mining, and data visualization applications. This paper presents a new family of histograms, the Hierarchical Model Fitting (HMF) histograms, based on the Minimum Description Length principle. Rather than having each bucket of a histogram described by the same type of model, the HMF histograms employ a local optimal model for each bucket. The improved effectiveness of the locally chosen models offsets more than the overhead of keeping track of the representation of each individual bucket. Through a set of experiments, we show that the HMF histograms are capable of providing more accurate approximations than previously proposed techniques for many real and synthetic data sets across a variety of query workloads. © Springer-Verlag 2006
abstract_unstemmed	Abstract Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many OLAP, data mining, and data visualization applications. This paper presents a new family of histograms, the Hierarchical Model Fitting (HMF) histograms, based on the Minimum Description Length principle. Rather than having each bucket of a histogram described by the same type of model, the HMF histograms employ a local optimal model for each bucket. The improved effectiveness of the locally chosen models offsets more than the overhead of keeping track of the representation of each individual bucket. Through a set of experiments, we show that the HMF histograms are capable of providing more accurate approximations than previously proposed techniques for many real and synthetic data sets across a variety of query workloads. © Springer-Verlag 2006
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title_short	Histograms based on the minimum description length principle
url	https://doi.org/10.1007/s00778-006-0015-0
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author2	Sevcik, Kenneth C.
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doi_str	10.1007/s00778-006-0015-0
up_date	2024-07-04T04:12:23.550Z
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