A Principled Approach to the Analysis of Process Mining Algorithms

Philip Weber; Behzad Bordbar; Peter Tino

doi:10.1007/978-3-642-23878-9_56

A Principled Approach to the Analysis of Process Mining Algorithms

Philip Weber, Behzad Bordbar, Peter Tino

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the behaviour of algorithms and the amount of data needed for confidence in mining. We contribute a first step towards a novel probabilistic framework within which to talk about approaches to process mining, and apply it to the well-known Alpha Algorithm. We show that knowledge of model structures and algorithm behaviour can be used to predict the number of traces needed for mining.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - IDEAL 2011
Subtitle of host publication	12th International Conference, Norwich, UK, September 7-9, 2011. Proceedings
Editors	H Yin, W Wang, V Rayward-Smith
Publisher	Springer
Pages	474-481
Number of pages	8
ISBN (Electronic)	978-3-642-23878-9
ISBN (Print)	978-3-642-23877-2
DOIs	https://doi.org/10.1007/978-3-642-23878-9_56
Publication status	Published - 1 Jan 2012
Event	12th International Conference on Intelligent Data Engineering and Automated Learning - Norwich, United Kingdom Duration: 7 Sept 2011 → 9 Sept 2011

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	6936
ISSN (Print)	0302-9743

Conference

Conference	12th International Conference on Intelligent Data Engineering and Automated Learning
Country/Territory	United Kingdom
City	Norwich
Period	7/09/11 → 9/09/11

Keywords

Business process mining
probabilistic automata
Petri nets

Access to Document

10.1007/978-3-642-23878-9_56

Unified probabilistic modelleing of adaptive spatial temporal structures in the human brain
Tino, P. & Kourtzi, Z.
Biotechnology & Biological Sciences Research Council
1/10/10 → 30/03/14
Project: Research Councils

Cite this

Weber, P., Bordbar, B., & Tino, P. (2012). A Principled Approach to the Analysis of Process Mining Algorithms. In H. Yin, W. Wang, & V. Rayward-Smith (Eds.), Intelligent Data Engineering and Automated Learning - IDEAL 2011: 12th International Conference, Norwich, UK, September 7-9, 2011. Proceedings (pp. 474-481). (Lecture Notes in Computer Science; Vol. 6936). Springer. https://doi.org/10.1007/978-3-642-23878-9_56

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title = "A Principled Approach to the Analysis of Process Mining Algorithms",

abstract = "Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the behaviour of algorithms and the amount of data needed for confidence in mining. We contribute a first step towards a novel probabilistic framework within which to talk about approaches to process mining, and apply it to the well-known Alpha Algorithm. We show that knowledge of model structures and algorithm behaviour can be used to predict the number of traces needed for mining.",

keywords = "Business process mining, probabilistic automata, Petri nets",

author = "Philip Weber and Behzad Bordbar and Peter Tino",

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doi = "10.1007/978-3-642-23878-9_56",

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Weber, P, Bordbar, B & Tino, P 2012, A Principled Approach to the Analysis of Process Mining Algorithms. in H Yin, W Wang & V Rayward-Smith (eds), Intelligent Data Engineering and Automated Learning - IDEAL 2011: 12th International Conference, Norwich, UK, September 7-9, 2011. Proceedings. Lecture Notes in Computer Science, vol. 6936, Springer, pp. 474-481, 12th International Conference on Intelligent Data Engineering and Automated Learning, Norwich, United Kingdom, 7/09/11. https://doi.org/10.1007/978-3-642-23878-9_56

A Principled Approach to the Analysis of Process Mining Algorithms. / Weber, Philip; Bordbar, Behzad; Tino, Peter.
Intelligent Data Engineering and Automated Learning - IDEAL 2011: 12th International Conference, Norwich, UK, September 7-9, 2011. Proceedings. ed. / H Yin; W Wang; V Rayward-Smith. Springer, 2012. p. 474-481 (Lecture Notes in Computer Science; Vol. 6936).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Principled Approach to the Analysis of Process Mining Algorithms

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AU - Bordbar, Behzad

AU - Tino, Peter

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N2 - Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the behaviour of algorithms and the amount of data needed for confidence in mining. We contribute a first step towards a novel probabilistic framework within which to talk about approaches to process mining, and apply it to the well-known Alpha Algorithm. We show that knowledge of model structures and algorithm behaviour can be used to predict the number of traces needed for mining.

AB - Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the behaviour of algorithms and the amount of data needed for confidence in mining. We contribute a first step towards a novel probabilistic framework within which to talk about approaches to process mining, and apply it to the well-known Alpha Algorithm. We show that knowledge of model structures and algorithm behaviour can be used to predict the number of traces needed for mining.

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KW - probabilistic automata

KW - Petri nets

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DO - 10.1007/978-3-642-23878-9_56

M3 - Conference contribution

SN - 978-3-642-23877-2

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EP - 481

BT - Intelligent Data Engineering and Automated Learning - IDEAL 2011

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Weber P, Bordbar B, Tino P. A Principled Approach to the Analysis of Process Mining Algorithms. In Yin H, Wang W, Rayward-Smith V, editors, Intelligent Data Engineering and Automated Learning - IDEAL 2011: 12th International Conference, Norwich, UK, September 7-9, 2011. Proceedings. Springer. 2012. p. 474-481. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-23878-9_56

A Principled Approach to the Analysis of Process Mining Algorithms

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Publication series

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Keywords

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Projects

Unified probabilistic modelleing of adaptive spatial temporal structures in the human brain

Cite this