dc.contributor.author |
Le, VBT |
en |
dc.contributor.author |
Link, Sebastian |
en |
dc.contributor.author |
Memari, Mozhgan |
en |
dc.date.accessioned |
2012-12-04T01:56:21Z |
en |
dc.date.issued |
2012-09 |
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dc.identifier.citation |
Journal of Computing Science and Engineering 6(3):193-206 Sep 2012 |
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dc.identifier.issn |
1976-4677 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/19700 |
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dc.description.abstract |
Keys play a fundamental role in all data models. They allow database systems to uniquely identify data items, and therefore, promote efficient data processing in many applications. Due to this, support is required to discover keys. These include keys that are semantically meaningful for the application domain, or are satisfied by a given database. We study the discovery of keys from SQL tables. We investigate the structural and computational properties of Armstrong tables for sets of SQL keys. Inspections of Armstrong tables enable data engineers to consolidate their understanding of semantically meaningful keys, and to communicate this understanding to other stakeholders. The stake-holders may want to make changes to the tables or provide entirely different tables to communicate their views to the data engineers. For such a purpose, we propose data mining algorithms that discover keys from a given SQL table. We combine the key mining algorithms with Armstrong table computations to generate informative Armstrong tables, that is, key-preserving semantic samples of existing SQL tables. Finally, we define formal measures to assess the distance between sets of SQL keys. The measures can be applied to validate the usefulness of Armstrong tables, and to automate the marking and feedback of non-multiple choice questions in database courses. |
en |
dc.publisher |
Korean Institute of Information Scientists and Engineers |
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dc.relation.ispartofseries |
Journal of Computing Science and Engineering |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
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dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.rights.uri |
http://creativecommons.org/licenses/by-nc/3.0/ |
en |
dc.subject |
Algorithm |
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dc.subject |
Armstrong database |
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dc.subject |
Complexity |
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dc.subject |
Key |
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dc.subject |
Soundness |
en |
dc.subject |
Completeness |
en |
dc.subject |
Mining |
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dc.title |
Schema- and Data-driven Discovery of SQL Keys |
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dc.type |
Journal Article |
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dc.identifier.doi |
10.5626/JCSE.2012.6.3.193 |
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pubs.issue |
3 |
en |
pubs.begin-page |
193 |
en |
pubs.volume |
6 |
en |
dc.rights.holder |
Copyright: 2012. The Korean Institute of Information Scientists and Engineers |
en |
pubs.end-page |
206 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Article |
en |
pubs.elements-id |
365830 |
en |
pubs.org-id |
Planning and Information |
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pubs.org-id |
Science |
en |
pubs.org-id |
School of Computer Science |
en |
dc.identifier.eissn |
2093-8020 |
en |
pubs.record-created-at-source-date |
2012-11-29 |
en |