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Data Transformation and Semantic Log Purging for Process Mining

Ly, Linh Thao and Indiono, Conrad and Mangler, Jürgen and Rinderle-Ma, Stefanie (2012) Data Transformation and Semantic Log Purging for Process Mining. In: 24th International Conference on Advanced Information Systems Engineering (CAiSE'12), 25-29 June 2012, Gdansk, Poland.

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Abstract

Existing process mining approaches are able to tolerate a certain degree of noise in the process log. However, processes that contain infrequent paths, multiple (nested) parallel branches, or have been changed in an ad-hoc manner, still pose major challenges. For such cases, process mining typically returns "spaghetti-models", that are hardly usable even as a starting point for process (re-)design. In this paper, we address these challenges by introducing data transformation and pre-processing steps that improve and ensure the quality of mined models for existing process mining approaches. We propose the concept of semantic log purging, the cleaning of logs based on domain specific constraints utilizing semantic knowledge which typically complements processes. Furthermore we demonstrate the feasibility and effectiveness of the approach based on a case study in the higher education domain. We think that semantic log purging will enable process mining to yield better results, thus giving process (re-)designers a valuable tool.

Item Type:Conference or Workshop Item (Paper)
Subjects:DBIS Research > Publications
ID Code:796
Deposited By: Linh Thao Ly
BibTex Export:BibTeX
Deposited On:27 Mar 2012 15:07
Last Modified:30 Sep 2012 17:12

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