$ CubiST^{++} $: Evaluating Ad-Hoc CUBE Queries Using Statistics Trees
Abstract We report on a new, efficient encoding for the data cube, which results in a drastic speed-up of OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes. We are focusing on a class of queries called cube queries, which return aggregated valu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hammer, Joachim [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2003 |
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| Anmerkung: |
© Kluwer Academic Publishers 2003 |
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| Übergeordnetes Werk: |
Enthalten in: Distributed and parallel databases - Kluwer Academic Publishers, 1993, 14(2003), 3 vom: Nov., Seite 221-254 |
|---|---|
| Übergeordnetes Werk: |
volume:14 ; year:2003 ; number:3 ; month:11 ; pages:221-254 |
| Links: |
|---|
| DOI / URN: |
10.1023/A:1025537315785 |
|---|
| Katalog-ID: |
OLC2027065652 |
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10.1023/A:1025537315785 doi (DE-627)OLC2027065652 (DE-He213)A:1025537315785-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn Hammer, Joachim verfasserin aut $ CubiST^{++} $: Evaluating Ad-Hoc CUBE Queries Using Statistics Trees 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract We report on a new, efficient encoding for the data cube, which results in a drastic speed-up of OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes. We are focusing on a class of queries called cube queries, which return aggregated values rather than sets of tuples. Our approach, termed $ CubiST^{++} $ (Cubing with Statistics Trees Plus Families), represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the view lattice to compute and materialize new views from existing views in some heuristic fashion. Instead, $ CubiST^{++} $ encodes all possible aggregate views in the leaves of a new data structure called statistics tree (ST) during a one-time scan of the detailed data. In order to optimize the queries involving constraints on hierarchy levels of the underlying dimensions, we select andmaterialize a family of candidate trees, which represent superviews over the different hierarchical levels of the dimensions. Given a query, our query evaluation algorithm selects the smallest tree in the family, which can provide the answer. Extensive evaluations of our prototype implementation have demonstrated its superior run-time performance and scalability when compared with existing MOLAP and ROLAP systems. Fu, Lixin aut Enthalten in Distributed and parallel databases Kluwer Academic Publishers, 1993 14(2003), 3 vom: Nov., Seite 221-254 (DE-627)165664401 (DE-600)913166-8 (DE-576)038480352 0926-8782 nnns volume:14 year:2003 number:3 month:11 pages:221-254 https://doi.org/10.1023/A:1025537315785 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-BBI GBV_ILN_31 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2021 GBV_ILN_2244 GBV_ILN_4036 GBV_ILN_4126 GBV_ILN_4266 GBV_ILN_4305 GBV_ILN_4307 GBV_ILN_4318 GBV_ILN_4326 AR 14 2003 3 11 221-254 |
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10.1023/A:1025537315785 doi (DE-627)OLC2027065652 (DE-He213)A:1025537315785-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn Hammer, Joachim verfasserin aut $ CubiST^{++} $: Evaluating Ad-Hoc CUBE Queries Using Statistics Trees 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract We report on a new, efficient encoding for the data cube, which results in a drastic speed-up of OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes. We are focusing on a class of queries called cube queries, which return aggregated values rather than sets of tuples. Our approach, termed $ CubiST^{++} $ (Cubing with Statistics Trees Plus Families), represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the view lattice to compute and materialize new views from existing views in some heuristic fashion. Instead, $ CubiST^{++} $ encodes all possible aggregate views in the leaves of a new data structure called statistics tree (ST) during a one-time scan of the detailed data. In order to optimize the queries involving constraints on hierarchy levels of the underlying dimensions, we select andmaterialize a family of candidate trees, which represent superviews over the different hierarchical levels of the dimensions. Given a query, our query evaluation algorithm selects the smallest tree in the family, which can provide the answer. Extensive evaluations of our prototype implementation have demonstrated its superior run-time performance and scalability when compared with existing MOLAP and ROLAP systems. Fu, Lixin aut Enthalten in Distributed and parallel databases Kluwer Academic Publishers, 1993 14(2003), 3 vom: Nov., Seite 221-254 (DE-627)165664401 (DE-600)913166-8 (DE-576)038480352 0926-8782 nnns volume:14 year:2003 number:3 month:11 pages:221-254 https://doi.org/10.1023/A:1025537315785 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-BBI GBV_ILN_31 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2021 GBV_ILN_2244 GBV_ILN_4036 GBV_ILN_4126 GBV_ILN_4266 GBV_ILN_4305 GBV_ILN_4307 GBV_ILN_4318 GBV_ILN_4326 AR 14 2003 3 11 221-254 |
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10.1023/A:1025537315785 doi (DE-627)OLC2027065652 (DE-He213)A:1025537315785-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn Hammer, Joachim verfasserin aut $ CubiST^{++} $: Evaluating Ad-Hoc CUBE Queries Using Statistics Trees 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract We report on a new, efficient encoding for the data cube, which results in a drastic speed-up of OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes. We are focusing on a class of queries called cube queries, which return aggregated values rather than sets of tuples. Our approach, termed $ CubiST^{++} $ (Cubing with Statistics Trees Plus Families), represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the view lattice to compute and materialize new views from existing views in some heuristic fashion. Instead, $ CubiST^{++} $ encodes all possible aggregate views in the leaves of a new data structure called statistics tree (ST) during a one-time scan of the detailed data. In order to optimize the queries involving constraints on hierarchy levels of the underlying dimensions, we select andmaterialize a family of candidate trees, which represent superviews over the different hierarchical levels of the dimensions. Given a query, our query evaluation algorithm selects the smallest tree in the family, which can provide the answer. Extensive evaluations of our prototype implementation have demonstrated its superior run-time performance and scalability when compared with existing MOLAP and ROLAP systems. Fu, Lixin aut Enthalten in Distributed and parallel databases Kluwer Academic Publishers, 1993 14(2003), 3 vom: Nov., Seite 221-254 (DE-627)165664401 (DE-600)913166-8 (DE-576)038480352 0926-8782 nnns volume:14 year:2003 number:3 month:11 pages:221-254 https://doi.org/10.1023/A:1025537315785 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-BBI GBV_ILN_31 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2021 GBV_ILN_2244 GBV_ILN_4036 GBV_ILN_4126 GBV_ILN_4266 GBV_ILN_4305 GBV_ILN_4307 GBV_ILN_4318 GBV_ILN_4326 AR 14 2003 3 11 221-254 |
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$ CubiST^{++} $: Evaluating Ad-Hoc CUBE Queries Using Statistics Trees |
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Hammer, Joachim |
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$ cubist^{++} $: evaluating ad-hoc cube queries using statistics trees |
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$ CubiST^{++} $: Evaluating Ad-Hoc CUBE Queries Using Statistics Trees |
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Abstract We report on a new, efficient encoding for the data cube, which results in a drastic speed-up of OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes. We are focusing on a class of queries called cube queries, which return aggregated values rather than sets of tuples. Our approach, termed $ CubiST^{++} $ (Cubing with Statistics Trees Plus Families), represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the view lattice to compute and materialize new views from existing views in some heuristic fashion. Instead, $ CubiST^{++} $ encodes all possible aggregate views in the leaves of a new data structure called statistics tree (ST) during a one-time scan of the detailed data. In order to optimize the queries involving constraints on hierarchy levels of the underlying dimensions, we select andmaterialize a family of candidate trees, which represent superviews over the different hierarchical levels of the dimensions. Given a query, our query evaluation algorithm selects the smallest tree in the family, which can provide the answer. Extensive evaluations of our prototype implementation have demonstrated its superior run-time performance and scalability when compared with existing MOLAP and ROLAP systems. © Kluwer Academic Publishers 2003 |
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Abstract We report on a new, efficient encoding for the data cube, which results in a drastic speed-up of OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes. We are focusing on a class of queries called cube queries, which return aggregated values rather than sets of tuples. Our approach, termed $ CubiST^{++} $ (Cubing with Statistics Trees Plus Families), represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the view lattice to compute and materialize new views from existing views in some heuristic fashion. Instead, $ CubiST^{++} $ encodes all possible aggregate views in the leaves of a new data structure called statistics tree (ST) during a one-time scan of the detailed data. In order to optimize the queries involving constraints on hierarchy levels of the underlying dimensions, we select andmaterialize a family of candidate trees, which represent superviews over the different hierarchical levels of the dimensions. Given a query, our query evaluation algorithm selects the smallest tree in the family, which can provide the answer. Extensive evaluations of our prototype implementation have demonstrated its superior run-time performance and scalability when compared with existing MOLAP and ROLAP systems. © Kluwer Academic Publishers 2003 |
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Abstract We report on a new, efficient encoding for the data cube, which results in a drastic speed-up of OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes. We are focusing on a class of queries called cube queries, which return aggregated values rather than sets of tuples. Our approach, termed $ CubiST^{++} $ (Cubing with Statistics Trees Plus Families), represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the view lattice to compute and materialize new views from existing views in some heuristic fashion. Instead, $ CubiST^{++} $ encodes all possible aggregate views in the leaves of a new data structure called statistics tree (ST) during a one-time scan of the detailed data. In order to optimize the queries involving constraints on hierarchy levels of the underlying dimensions, we select andmaterialize a family of candidate trees, which represent superviews over the different hierarchical levels of the dimensions. Given a query, our query evaluation algorithm selects the smallest tree in the family, which can provide the answer. Extensive evaluations of our prototype implementation have demonstrated its superior run-time performance and scalability when compared with existing MOLAP and ROLAP systems. © Kluwer Academic Publishers 2003 |
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