A framework for application of tree-structured data mining to process log analysis

Many data mining and simulation based algorithms have been applied in the process mining field; nevertheless they mainly focus on the process discovery and conformance checking tasks. Even though the event logs are increasingly represented in semi-structured format using XML-based templates, commonl...

Full description

Bibliographic Details
Main Authors: Bui, Dang, Hadzic, Fedja, Potdar, Vidyasagar
Other Authors: Hujin, Y.
Format: Conference Paper
Published: Springer Verlag 2012
Online Access:http://hdl.handle.net/20.500.11937/19696
_version_ 1848750104499978240
author Bui, Dang
Hadzic, Fedja
Potdar, Vidyasagar
author2 Hujin, Y.
author_facet Hujin, Y.
Bui, Dang
Hadzic, Fedja
Potdar, Vidyasagar
author_sort Bui, Dang
building Curtin Institutional Repository
collection Online Access
description Many data mining and simulation based algorithms have been applied in the process mining field; nevertheless they mainly focus on the process discovery and conformance checking tasks. Even though the event logs are increasingly represented in semi-structured format using XML-based templates, commonly used XML mining techniques have not been explored. In this paper, we investigate the application of tree mining techniques and propose a general framework, within which a wider range of structure aware data mining techniques can be applied. Decision tree learning and frequent pattern mining are used as a case in point in the experiments on publicly available real dataset. The results indicate the promising properties of the proposed framework in adding to the available set of tools for process log analysis by enabling (i) direct data mining of tree-structured process logs (ii) extraction of informative knowledge patterns and (iii) frequent pattern mining at lower minimum support thresholds.
first_indexed 2025-11-14T07:31:32Z
format Conference Paper
id curtin-20.500.11937-19696
institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:31:32Z
publishDate 2012
publisher Springer Verlag
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-196962023-02-02T07:57:39Z A framework for application of tree-structured data mining to process log analysis Bui, Dang Hadzic, Fedja Potdar, Vidyasagar Hujin, Y. Costa, J.A.F. Barreto, G. Many data mining and simulation based algorithms have been applied in the process mining field; nevertheless they mainly focus on the process discovery and conformance checking tasks. Even though the event logs are increasingly represented in semi-structured format using XML-based templates, commonly used XML mining techniques have not been explored. In this paper, we investigate the application of tree mining techniques and propose a general framework, within which a wider range of structure aware data mining techniques can be applied. Decision tree learning and frequent pattern mining are used as a case in point in the experiments on publicly available real dataset. The results indicate the promising properties of the proposed framework in adding to the available set of tools for process log analysis by enabling (i) direct data mining of tree-structured process logs (ii) extraction of informative knowledge patterns and (iii) frequent pattern mining at lower minimum support thresholds. 2012 Conference Paper http://hdl.handle.net/20.500.11937/19696 10.1007/978-3-642-32639-4_52 Springer Verlag restricted
spellingShingle Bui, Dang
Hadzic, Fedja
Potdar, Vidyasagar
A framework for application of tree-structured data mining to process log analysis
title A framework for application of tree-structured data mining to process log analysis
title_full A framework for application of tree-structured data mining to process log analysis
title_fullStr A framework for application of tree-structured data mining to process log analysis
title_full_unstemmed A framework for application of tree-structured data mining to process log analysis
title_short A framework for application of tree-structured data mining to process log analysis
title_sort framework for application of tree-structured data mining to process log analysis
url http://hdl.handle.net/20.500.11937/19696