Discovering Process Reference Models from Process Variants Using Clustering Techniques

C. Li, M.U. Reichert, Andreas Wombacher

Research output: Book/ReportReportProfessional

138 Downloads (Pure)

Abstract

In today's dynamic business world, success of an enterprise increasingly depends on its ability to react to changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, has led to large number of process and service variants derived from the same model, but differs in structures due to the applied changes. This paper provides a sophisticated approach which fosters learning from past process changes and allows for determining such process variants. As a result we obtain a generic process model for which the average distances between this model and the process variants becomes minimal. By adopting this generic process model in the PAIS, need for future process configuration and adaptation will decrease. The mining method proposed has been implemented in a powerful proof-of-concept prototype and further validated by a comparison between other process mining algorithms.
Original languageUndefined
Place of PublicationEnschede
PublisherCentre for Telematics and Information Technology (CTIT)
Number of pages30
Publication statusPublished - 31 Mar 2008

Publication series

NameCTIT Technical Report Series
PublisherCentre for Telematics and Information Technology, University of Twente
No.69160R/TR-CTIT-08-30
ISSN (Print)1381-3625

Keywords

  • SCS-Services
  • EWI-12180
  • IR-64701
  • METIS-250933

Cite this

Li, C., Reichert, M. U., & Wombacher, A. (2008). Discovering Process Reference Models from Process Variants Using Clustering Techniques. (CTIT Technical Report Series; No. 69160R/TR-CTIT-08-30). Enschede: Centre for Telematics and Information Technology (CTIT).
Li, C. ; Reichert, M.U. ; Wombacher, Andreas. / Discovering Process Reference Models from Process Variants Using Clustering Techniques. Enschede : Centre for Telematics and Information Technology (CTIT), 2008. 30 p. (CTIT Technical Report Series; 69160R/TR-CTIT-08-30).
@book{00fc4aef672249ef960007a93d7ce1bf,
title = "Discovering Process Reference Models from Process Variants Using Clustering Techniques",
abstract = "In today's dynamic business world, success of an enterprise increasingly depends on its ability to react to changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, has led to large number of process and service variants derived from the same model, but differs in structures due to the applied changes. This paper provides a sophisticated approach which fosters learning from past process changes and allows for determining such process variants. As a result we obtain a generic process model for which the average distances between this model and the process variants becomes minimal. By adopting this generic process model in the PAIS, need for future process configuration and adaptation will decrease. The mining method proposed has been implemented in a powerful proof-of-concept prototype and further validated by a comparison between other process mining algorithms.",
keywords = "SCS-Services, EWI-12180, IR-64701, METIS-250933",
author = "C. Li and M.U. Reichert and Andreas Wombacher",
year = "2008",
month = "3",
day = "31",
language = "Undefined",
series = "CTIT Technical Report Series",
publisher = "Centre for Telematics and Information Technology (CTIT)",
number = "69160R/TR-CTIT-08-30",
address = "Netherlands",

}

Li, C, Reichert, MU & Wombacher, A 2008, Discovering Process Reference Models from Process Variants Using Clustering Techniques. CTIT Technical Report Series, no. 69160R/TR-CTIT-08-30, Centre for Telematics and Information Technology (CTIT), Enschede.

Discovering Process Reference Models from Process Variants Using Clustering Techniques. / Li, C.; Reichert, M.U.; Wombacher, Andreas.

Enschede : Centre for Telematics and Information Technology (CTIT), 2008. 30 p. (CTIT Technical Report Series; No. 69160R/TR-CTIT-08-30).

Research output: Book/ReportReportProfessional

TY - BOOK

T1 - Discovering Process Reference Models from Process Variants Using Clustering Techniques

AU - Li, C.

AU - Reichert, M.U.

AU - Wombacher, Andreas

PY - 2008/3/31

Y1 - 2008/3/31

N2 - In today's dynamic business world, success of an enterprise increasingly depends on its ability to react to changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, has led to large number of process and service variants derived from the same model, but differs in structures due to the applied changes. This paper provides a sophisticated approach which fosters learning from past process changes and allows for determining such process variants. As a result we obtain a generic process model for which the average distances between this model and the process variants becomes minimal. By adopting this generic process model in the PAIS, need for future process configuration and adaptation will decrease. The mining method proposed has been implemented in a powerful proof-of-concept prototype and further validated by a comparison between other process mining algorithms.

AB - In today's dynamic business world, success of an enterprise increasingly depends on its ability to react to changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, has led to large number of process and service variants derived from the same model, but differs in structures due to the applied changes. This paper provides a sophisticated approach which fosters learning from past process changes and allows for determining such process variants. As a result we obtain a generic process model for which the average distances between this model and the process variants becomes minimal. By adopting this generic process model in the PAIS, need for future process configuration and adaptation will decrease. The mining method proposed has been implemented in a powerful proof-of-concept prototype and further validated by a comparison between other process mining algorithms.

KW - SCS-Services

KW - EWI-12180

KW - IR-64701

KW - METIS-250933

M3 - Report

T3 - CTIT Technical Report Series

BT - Discovering Process Reference Models from Process Variants Using Clustering Techniques

PB - Centre for Telematics and Information Technology (CTIT)

CY - Enschede

ER -

Li C, Reichert MU, Wombacher A. Discovering Process Reference Models from Process Variants Using Clustering Techniques. Enschede: Centre for Telematics and Information Technology (CTIT), 2008. 30 p. (CTIT Technical Report Series; 69160R/TR-CTIT-08-30).