dc.contributor.author |
Tran Le, VB |
en |
dc.contributor.author |
Link, S |
en |
dc.contributor.author |
Ferrarotti, F |
en |
dc.date.accessioned |
2014-01-05T22:48:17Z |
en |
dc.date.available |
2014-01-05T22:48:17Z |
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dc.date.issued |
2013 |
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dc.identifier.citation |
CDMTCS Research Reports CDMTCS-451 (2013) |
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dc.identifier.issn |
1178-3540 |
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dc.identifier.uri |
http://hdl.handle.net/2292/21350 |
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dc.description.abstract |
SQL designs result from methodologies such as UML or Entity-Relationship
models, description logics, or relational normalization. Independently of the methodology, sample data is promoted by academia and industry to visualize and consolidate the designs produced. SQL table definitions are a standard-compliant encoding of their designers' perception about the semantics of an application domain.
Armstrong sample data visualize these perceptions. We present a tool that computes Armstrong samples for different classes of SQL constraints. Exploiting our tool, we then investigate empirically how these Armstrong samples help design
teams recognize domain semantics. New measures empower us to compute the
distance between constraint sets in order to evaluate the usefulness of our tool.
Extensive experiments con rm that users of our tool are likely to recognize domain
semantics they would overlook otherwise. The tool thereby e ffectively complements existing design methodologies in finding quality schemata that process data efficiently. |
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dc.publisher |
Department of Computer Science, The University of Auckland, New Zealand |
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dc.relation.ispartofseries |
CDMTCS Research Report Series |
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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. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.source.uri |
http://www.cs.auckland.ac.nz/staff-cgi-bin/mjd/secondcgi.pl?serial |
en |
dc.title |
Effective Recognition and Visualization of Semantic Requirements by Perfect SQL Samples |
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dc.type |
Technical Report |
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dc.subject.marsden |
Fields of Research::280000 Information, Computing and Communication Sciences |
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dc.rights.holder |
The author(s) |
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dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |