TU Darmstadt / ULB / TUbiblio

Can We Find Better Process Models? Process Model Improvement using Motif-based Graph Adaptation

Seeliger, Alexander and Stein, Michael and Mühlhäuser, Max
Teniente, Ernest and Weidlich, Matthias (eds.) :

Can We Find Better Process Models? Process Model Improvement using Motif-based Graph Adaptation.
In: Business Process Management Workshops. BPM 2017. Lecture Notes in Business Information Processing Springer International Publishing
[Conference or Workshop Item] , (2017) , 308

Abstract

In today’s organizations efficient and reliable business processes have a high influence on success. Organizations spend high effort in analyzing processes to stay in front of the competition. However, in practice it is a huge challenge to find better processes based on process mining results due to the high complexity of the underlying model. This paper presents a novel approach which provides suggestions for redesigning business processes by using discovered as-is process models from event logs and apply motif-based graph adaptation. Motifs are graph patterns of small size, building the core blocks of graphs. Our approach uses the LoMbA algorithm, which takes a desired motif frequency distribution and adjusts the model to fit that distribution under the consideration of side constraints. The paper presents the underlying concepts, discusses how the motif distribution can be selected and shows the applicability using real-life event logs. Our results show that motif-based graph adaptation adjusts process graphs towards defined improvement goals.

Item Type: Conference or Workshop Item
Erschienen: 2017
Editors: Teniente, Ernest and Weidlich, Matthias
Creators: Seeliger, Alexander and Stein, Michael and Mühlhäuser, Max
Title: Can We Find Better Process Models? Process Model Improvement using Motif-based Graph Adaptation
Language: English
Abstract:

In today’s organizations efficient and reliable business processes have a high influence on success. Organizations spend high effort in analyzing processes to stay in front of the competition. However, in practice it is a huge challenge to find better processes based on process mining results due to the high complexity of the underlying model. This paper presents a novel approach which provides suggestions for redesigning business processes by using discovered as-is process models from event logs and apply motif-based graph adaptation. Motifs are graph patterns of small size, building the core blocks of graphs. Our approach uses the LoMbA algorithm, which takes a desired motif frequency distribution and adjusts the model to fit that distribution under the consideration of side constraints. The paper presents the underlying concepts, discusses how the motif distribution can be selected and shows the applicability using real-life event logs. Our results show that motif-based graph adaptation adjusts process graphs towards defined improvement goals.

Journal or Publication Title: Business Process Management Workshops. BPM 2017. Lecture Notes in Business Information Processing
Title of Book: Business Process Management Workshops
Publisher: Springer International Publishing
Edition: 308
Uncontrolled Keywords: Business process optimization, Graph adaptation, Business process analytics, Data mining, Tool support
Divisions: Department of Computer Science
Department of Computer Science > Telecooperation
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology > Subproject A1: Modelling
Event Location: Barcelona, Spain
Date Deposited: 04 Aug 2017 09:31
DOI: 10.1007/978-3-319-74030-0_17
Export:

Optionen (nur für Redakteure)

View Item View Item