Mining complex structured data: Enhanced methods and applications
Conventional approaches to analysing complex business data typically rely on process models, which are difficult to construct and use. This thesis addresses this issue by converting semi-structured event logs to a simpler flat representation without any loss of information, which then enables direct...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
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Curtin University
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/480 |
| _version_ | 1848743390554882048 |
|---|---|
| author | Bui, Dang Bach |
| author_facet | Bui, Dang Bach |
| author_sort | Bui, Dang Bach |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Conventional approaches to analysing complex business data typically rely on process models, which are difficult to construct and use. This thesis addresses this issue by converting semi-structured event logs to a simpler flat representation without any loss of information, which then enables direct applications of classical data mining methods. The thesis also proposes an effective and scalable classification method which can identify distinct characteristics of a business process for further improvements. |
| first_indexed | 2025-11-14T05:44:49Z |
| format | Thesis |
| id | curtin-20.500.11937-480 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:44:49Z |
| publishDate | 2015 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-4802017-02-20T06:42:28Z Mining complex structured data: Enhanced methods and applications Bui, Dang Bach Conventional approaches to analysing complex business data typically rely on process models, which are difficult to construct and use. This thesis addresses this issue by converting semi-structured event logs to a simpler flat representation without any loss of information, which then enables direct applications of classical data mining methods. The thesis also proposes an effective and scalable classification method which can identify distinct characteristics of a business process for further improvements. 2015 Thesis http://hdl.handle.net/20.500.11937/480 en Curtin University fulltext |
| spellingShingle | Bui, Dang Bach Mining complex structured data: Enhanced methods and applications |
| title | Mining complex structured data: Enhanced methods and applications |
| title_full | Mining complex structured data: Enhanced methods and applications |
| title_fullStr | Mining complex structured data: Enhanced methods and applications |
| title_full_unstemmed | Mining complex structured data: Enhanced methods and applications |
| title_short | Mining complex structured data: Enhanced methods and applications |
| title_sort | mining complex structured data: enhanced methods and applications |
| url | http://hdl.handle.net/20.500.11937/480 |