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Handling summary information in a database: derivability

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Hideto SatoAuthors Info & Claims

SIGMOD '81: Proceedings of the 1981 ACM SIGMOD international conference on Management of data

Pages 98 - 107

https://doi.org/10.1145/582318.582334

Published: 29 April 1981 Publication History

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Abstract

"Summary data" is a representation of "groups of facts." Statistics are a typical example of summary data, which is often a major component of databases that deal with huge domains, such as objects in a whole country or events that occurred over a long time range. Although any summary can be reproduced from the corresponding originals, these are often unavailable and the required data may or may not be derivable from the given summary data. A schema of summary data is defined as a relationship between classifications of object types and domains for attributes. Reclassification rules are introduced as semantic relations among classifications. Set theoretical lemmata provide an inference mechanism that judges derivability of required summary data from collected summary data, and derives the former, if it is derivable, from the latter. It is also shown how this inference mechanism improves a summary database in usability and logical data independence. Discussions are made with examples in the statistical field.

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SIGMOD '81: Proceedings of the 1981 ACM SIGMOD international conference on Management of data

April 1981

237 pages

ISBN:0897910400

DOI:10.1145/582318

Conference Chair:
Toby J. Teorey
University of Michigan
,
Program Chair:
Y. Edmund Lien
Bell Laboratories

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Association for Computing Machinery

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Published: 29 April 1981

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Torlone R(2018)Interoperability in Data WarehousesEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_207(2021-2026)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_207
Torlone R(2016)Interoperability in Data WarehousesEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_207-2(1-6)Online publication date: 25-Oct-2016
https://doi.org/10.1007/978-1-4899-7993-3_207-2
Olaru M(2012)Partial multi-dimensional schema merging in heterogeneous data warehousesProceedings of the 31st international conference on Conceptual Modeling10.1007/978-3-642-34002-4_46(563-571)Online publication date: 15-Oct-2012
https://dl.acm.org/doi/10.1007/978-3-642-34002-4_46
Diamantini CPotena D(2011)Thinking Structurally Helps Business Intelligence DesignInformation Technology and Innovation Trends in Organizations10.1007/978-3-7908-2632-6_13(109-116)Online publication date: 9-May-2011
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Torlone R(2009)Interoperability in Data WarehousesEncyclopedia of Database Systems10.1007/978-0-387-39940-9_207(1560-1564)Online publication date: 2009
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Xie JYang JChen YWang HYu P(2008)A Sampling-Based Approach to Information RecoveryProceedings of the 2008 IEEE 24th International Conference on Data Engineering10.1109/ICDE.2008.4497456(476-485)Online publication date: 7-Apr-2008
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Torlone R(2008)Two approaches to the integration of heterogeneous data warehousesDistributed and Parallel Databases10.1007/s10619-007-7022-z23:1(69-97)Online publication date: 1-Feb-2008
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Malvestuto FMezzini MMoscarini M(2007)An analytical approach to the inference of summary data of additive typeTheoretical Computer Science10.1016/j.tcs.2007.07.004385:1-3(264-285)Online publication date: 1-Oct-2007
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Rafanelli MRicci F(2005)A visual interface for browsing and manipulating statistical entitiesStatistical and Scientific Database Management10.1007/3-540-52342-1_27(163-182)Online publication date: 9-Jun-2005
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Ahn TJo HKim JLee YKim B(2005)Temporal summary table management and graphic interfaceStatistical and Scientific Database Management10.1007/3-540-52342-1_24(112-130)Online publication date: 9-Jun-2005
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OCP Oracle Database 11g: Administration II Exam Guide (Exam 1Z0-053)

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