Data Mining to Support Engineering Design Decision
Data Mining to Support Engineering Design Decision
The design and maintenance of an aero-engine generates a significant amount of documentation. When designing new engines, engineers must obtain knowledge gained from maintenance of existing engines to identify possible areas of concern. Firstly, this paper investigate the use of advanced business intelligence tenchniques to solve the problem of knowledge transfer from maintenance to design of aeroengines. Based on data availability and quality, various models were deployed. An association model was used to uncover hidden trends among parts involved in maintenance events. Classification techniques comprising of various algorithms was employed to determine severity of events. Causes of high severity events that lead to major financial loss was traced with the help of summarization techniques. Secondly this paper compares and evaluates the business intelligence approach to solve the problem of knowledge transfer with solutions available from the Semantic Web. The results obtained provide a compelling need to have data mining support on RDF/OWL-based warehoused data.
Jadhav, Pooja
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Wong, Sylvia C
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Wills, Gary B
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Crowder, Richard M
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Shadbolt, Nigel R
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November 2007
Jadhav, Pooja
045db75c-10f4-4b4b-b7e4-fe786ffd3ee3
Wong, Sylvia C
7ae740e6-e789-4fd0-aa72-e4389012851f
Wills, Gary B
3a594558-6921-4e82-8098-38cd8d4e8aa0
Crowder, Richard M
ddeb646d-cc9e-487b-bd84-e1726d3ac023
Shadbolt, Nigel R
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
Jadhav, Pooja, Wong, Sylvia C, Wills, Gary B, Crowder, Richard M and Shadbolt, Nigel R
(2007)
Data Mining to Support Engineering Design Decision.
Workshop on Semantic Web and Web 2.0 in Architectural, Product and Engineering Design, 6th International Semantic Web Conference (ISWC), Busan, Korea.
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Abstract
The design and maintenance of an aero-engine generates a significant amount of documentation. When designing new engines, engineers must obtain knowledge gained from maintenance of existing engines to identify possible areas of concern. Firstly, this paper investigate the use of advanced business intelligence tenchniques to solve the problem of knowledge transfer from maintenance to design of aeroengines. Based on data availability and quality, various models were deployed. An association model was used to uncover hidden trends among parts involved in maintenance events. Classification techniques comprising of various algorithms was employed to determine severity of events. Causes of high severity events that lead to major financial loss was traced with the help of summarization techniques. Secondly this paper compares and evaluates the business intelligence approach to solve the problem of knowledge transfer with solutions available from the Semantic Web. The results obtained provide a compelling need to have data mining support on RDF/OWL-based warehoused data.
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Published date: November 2007
Additional Information:
Event Dates: 11 November 2007
Venue - Dates:
Workshop on Semantic Web and Web 2.0 in Architectural, Product and Engineering Design, 6th International Semantic Web Conference (ISWC), Busan, Korea, 2007-11-11
Organisations:
Web & Internet Science, Agents, Interactions & Complexity, Electronic & Software Systems
Identifiers
Local EPrints ID: 264807
URI: http://eprints.soton.ac.uk/id/eprint/264807
PURE UUID: add8f779-abb0-400c-accb-0fd05b9a5c04
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Date deposited: 12 Nov 2007 23:48
Last modified: 15 Mar 2024 02:51
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Contributors
Author:
Pooja Jadhav
Author:
Sylvia C Wong
Author:
Gary B Wills
Author:
Richard M Crowder
Author:
Nigel R Shadbolt
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