Application of tree-structured data mining for analysis of process logs in XML format

Process logs are increasingly being represented using XML based templates such as MXML and XES. Popular XML data mining techniques have had limited application to directly mine such data. The majority of work in the process mining field focuses on process discovery and conformance checking tasks oft...

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Bibliographic Details
Main Authors: Bui, Dang, Hadzic, Fedja, Hecker, Michael
Other Authors: Zhao, Y.
Format: Conference Paper
Published: Australian Computer Society 2012
Online Access:http://crpit.com/abstracts/CRPITV134Bul.html
http://hdl.handle.net/20.500.11937/17288
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author Bui, Dang
Hadzic, Fedja
Hecker, Michael
author2 Zhao, Y.
author_facet Zhao, Y.
Bui, Dang
Hadzic, Fedja
Hecker, Michael
author_sort Bui, Dang
building Curtin Institutional Repository
collection Online Access
description Process logs are increasingly being represented using XML based templates such as MXML and XES. Popular XML data mining techniques have had limited application to directly mine such data. The majority of work in the process mining field focuses on process discovery and conformance checking tasks often utilizing visualization and simulation based techniques. In this paper, an approach is proposed within which a wider range of data mining methods can be directly applied on tree-structured process log data. Clustering, classification and frequent pattern mining are used as a case in point and experiments are performed on publicly available real-world and synthetic data. The results indicate the great potential of the proposed approach in adding to the available set of methods for process log analysis. It presents an alternative where process model discovery is not the pre-requisite and a variety of methods can be directly applied.
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format Conference Paper
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:20:44Z
publishDate 2012
publisher Australian Computer Society
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spelling curtin-20.500.11937-172882023-02-02T07:57:39Z Application of tree-structured data mining for analysis of process logs in XML format Bui, Dang Hadzic, Fedja Hecker, Michael Zhao, Y. Li, J. Kennedy, P. J. Christen, P. Process logs are increasingly being represented using XML based templates such as MXML and XES. Popular XML data mining techniques have had limited application to directly mine such data. The majority of work in the process mining field focuses on process discovery and conformance checking tasks often utilizing visualization and simulation based techniques. In this paper, an approach is proposed within which a wider range of data mining methods can be directly applied on tree-structured process log data. Clustering, classification and frequent pattern mining are used as a case in point and experiments are performed on publicly available real-world and synthetic data. The results indicate the great potential of the proposed approach in adding to the available set of methods for process log analysis. It presents an alternative where process model discovery is not the pre-requisite and a variety of methods can be directly applied. 2012 Conference Paper http://hdl.handle.net/20.500.11937/17288 http://crpit.com/abstracts/CRPITV134Bul.html Australian Computer Society restricted
spellingShingle Bui, Dang
Hadzic, Fedja
Hecker, Michael
Application of tree-structured data mining for analysis of process logs in XML format
title Application of tree-structured data mining for analysis of process logs in XML format
title_full Application of tree-structured data mining for analysis of process logs in XML format
title_fullStr Application of tree-structured data mining for analysis of process logs in XML format
title_full_unstemmed Application of tree-structured data mining for analysis of process logs in XML format
title_short Application of tree-structured data mining for analysis of process logs in XML format
title_sort application of tree-structured data mining for analysis of process logs in xml format
url http://crpit.com/abstracts/CRPITV134Bul.html
http://hdl.handle.net/20.500.11937/17288