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Compressed data cubes for OLAP aggregate query approximation on continuous dimensions

Authors:

Jayavel Shanmugasundaram,

Usama Fayyad,

P. S. BradleyAuthors Info & Claims

KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 223 - 232

https://doi.org/10.1145/312129.312231

Published: 01 August 1999 Publication History

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Reznichenko V(2022)60 Years of Databases (part three)PROBLEMS IN PROGRAMMING10.15407/pp2022.01.034(034-066)Online publication date: Mar-2022
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Chakraborty S(2022)A Novel Approach of Using Materialized Queries for Retrieving Results from Data WarehouseProceedings of the International Conference on Intelligent Vision and Computing (ICIVC 2021)10.1007/978-3-030-97196-0_3(22-35)Online publication date: 24-Mar-2022
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Compressed data cubes for OLAP aggregate query approximation on continuous dimensions

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KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining

August 1999

439 pages

ISBN:1581131437

DOI:10.1145/312129

Chairmen:
Usama Fayyad
Microsoft Research
,
Surajit Chaudhuri
Microsoft Research
,
David Madigan
AT&T Labs-Research

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 August 1999

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August 15 - 18, 1999

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https://doi.org/10.15407/pp2022.01.034
Chakraborty S(2022)A Novel Approach of Using Materialized Queries for Retrieving Results from Data WarehouseProceedings of the International Conference on Intelligent Vision and Computing (ICIVC 2021)10.1007/978-3-030-97196-0_3(22-35)Online publication date: 24-Mar-2022
https://doi.org/10.1007/978-3-030-97196-0_3
Hilprecht BSchmidt AKulessa MMolina AKersting KBinnig C(2020)DeepDBProceedings of the VLDB Endowment10.14778/3384345.338434913:7(992-1005)Online publication date: 26-Mar-2020
https://dl.acm.org/doi/10.14778/3384345.3384349
Chakraborty SDoshi J(2020)Faster Result Retrieval from Health Care Product Sales Data Warehouse Using Materialized QueriesEvolution in Computational Intelligence10.1007/978-981-15-5788-0_1(1-9)Online publication date: 9-Sep-2020
https://doi.org/10.1007/978-981-15-5788-0_1
Lin CLi JWang XLu XZhang J(2020)WFApprox: Approximate Window Functions ProcessingDatabase Systems for Advanced Applications10.1007/978-3-030-59410-7_5(72-87)Online publication date: 18-Sep-2020
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Ma QTriantafillou PBoncz PManegold SAilamaki ADeshpande AKraska T(2019)DBEstProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3324958(1553-1570)Online publication date: 25-Jun-2019
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Pennino DPizzonia MPapi A(2019)Overlay Indexes: Efficiently Supporting Aggregate Range Queries and Authenticated Data Structures in Off-the-Shelf DatabasesIEEE Access10.1109/ACCESS.2019.29573467(175642-175670)Online publication date: 2019
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Gan EDing JTai KSharan VBailis P(2018)Moment-based quantile sketches for efficient high cardinality aggregation queriesProceedings of the VLDB Endowment10.14778/3236187.323621211:11(1647-1660)Online publication date: 1-Jul-2018
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Improving range-sum query evaluation on data cubes via polynomial approximation

Approximate range---sum query answering on data cubes with probabilistic guarantees

Compressed data cube for approximate OLAP query processing

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Improving range-sum query evaluation on data cubes via polynomial approximation

Approximate range---sum query answering on data cubes with probabilistic guarantees

Compressed data cube for approximate OLAP query processing

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