A framework for comparing process mining algorithms

Philip Weber; Behzad Bordbar; Peter Tino; B Majeed

doi:10.1109/IEEEGCC.2011.5752616

A framework for comparing process mining algorithms

Philip Weber, Behzad Bordbar, Peter Tino, B Majeed

Computer Science

Research output: Contribution to conference (unpublished) › Paper › peer-review

13 Citations (Scopus)

Abstract

There are many process mining algorithms with different theoretical foundations and aims, raising the question of how to choose the best for a particular situation. A framework is proposed for objectively comparing algorithms for process discovery against a known ground truth, with an implementation using existing tools. Results from an experimental evaluation of five algorithms against basic process structures confirm the validity of the approach. In general, numbers of traces for mining are predictable from the structure and probabilities in the model, but there are some algorithm-specific differences.

Original language	English
Pages	625-628
Number of pages	4
DOIs	https://doi.org/10.1109/IEEEGCC.2011.5752616
Publication status	Published - 22 Feb 2011
Event	Proceedings of the IEEE GCC Conference and Exhibition (GCC 2011) - Duration: 22 Feb 2011 → …

Conference

Conference	Proceedings of the IEEE GCC Conference and Exhibition (GCC 2011)
Period	22/02/11 → …

Keywords

business data processing
data mining
process mining algorithm
business data processing
process discovery

Access to Document

10.1109/IEEEGCC.2011.5752616

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

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title = "A framework for comparing process mining algorithms",

abstract = "There are many process mining algorithms with different theoretical foundations and aims, raising the question of how to choose the best for a particular situation. A framework is proposed for objectively comparing algorithms for process discovery against a known ground truth, with an implementation using existing tools. Results from an experimental evaluation of five algorithms against basic process structures confirm the validity of the approach. In general, numbers of traces for mining are predictable from the structure and probabilities in the model, but there are some algorithm-specific differences.",

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KW - business data processing

KW - data mining

KW - process mining algorithm

KW - business data processing

KW - process discovery

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A framework for comparing process mining algorithms

Abstract

Conference

Keywords

Access to Document

Projects

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

Cite this