A new framework for business process knowledge discovery

Hyerim Bae; Wonchang Hur; Sajal K. Das; Seoung Bum Kim

A new framework for business process knowledge discovery

Hyerim Bae, Wonchang Hur, Sajal K. Das, Seoung Bum Kim

Research output: Contribution to conference › Paper › peer-review

Abstract

The Business Process Management System (BPMS) has received more attention as companies increasingly realize the importance of business processes. However, traditional BPMS focuses mainly on correct modeling and exact automation of the process flow and pays little attention to the achievement of the final goals of improving process efficiency and process innovation. During and after execution of processes, BPMS usually generates much process log data in which numerous meaningful rules and patterns are hidden. In the present study we employ a data mining technique to extract useful knowledge from the complex process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and of the framework.

Original language	English
Pages	1037-1042
Number of pages	6
Publication status	Published - 2008
Externally published	Yes
Event	IIE Annual Conference and Expo 2008 - Vancouver, BC, Canada Duration: 2008 May 17 → 2008 May 21

Other

Other	IIE Annual Conference and Expo 2008
Country/Territory	Canada
City	Vancouver, BC
Period	08/5/17 → 08/5/21

Keywords

Business process
Business process mining
Decision tree
Process pattern

ASJC Scopus subject areas

Computer Science Applications
Software
Industrial and Manufacturing Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@conference{b25d9d669c214003816994832961e322,

title = "A new framework for business process knowledge discovery",

abstract = "The Business Process Management System (BPMS) has received more attention as companies increasingly realize the importance of business processes. However, traditional BPMS focuses mainly on correct modeling and exact automation of the process flow and pays little attention to the achievement of the final goals of improving process efficiency and process innovation. During and after execution of processes, BPMS usually generates much process log data in which numerous meaningful rules and patterns are hidden. In the present study we employ a data mining technique to extract useful knowledge from the complex process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and of the framework.",

keywords = "Business process, Business process mining, Decision tree, Process pattern",

author = "Hyerim Bae and Wonchang Hur and Das, {Sajal K.} and Kim, {Seoung Bum}",

note = "Funding Information: Silvia Ferrari (sferrari@duke.edu) is an assistant professor of mechanical engineering and materials science at Duke University, where she directs the Laboratory for Intelligent Systems and Controls. Her research interests include robust adaptive control of aircraft, learning and approximate dynamic programming, and distributed sensor management. She received the B.S. from Embry-Riddle Aeronautical University and the M.A. and Ph.D. from Princeton University. She is a member of IEEE, ASME, SPIE, and AIAA. She is the recipient of the ONR young investigator award (2004), the NSF CAREER award (2005), and the PECASE award (2006). She can be contacted at Duke University, Department of Mechanical Engineering and Materials Science, Box 90300, Durham, NC 27708-0005 USA.; IIE Annual Conference and Expo 2008 ; Conference date: 17-05-2008 Through 21-05-2008",

year = "2008",

language = "English",

pages = "1037--1042",

}

TY - CONF

T1 - A new framework for business process knowledge discovery

AU - Bae, Hyerim

AU - Hur, Wonchang

AU - Das, Sajal K.

AU - Kim, Seoung Bum

N1 - Funding Information: Silvia Ferrari (sferrari@duke.edu) is an assistant professor of mechanical engineering and materials science at Duke University, where she directs the Laboratory for Intelligent Systems and Controls. Her research interests include robust adaptive control of aircraft, learning and approximate dynamic programming, and distributed sensor management. She received the B.S. from Embry-Riddle Aeronautical University and the M.A. and Ph.D. from Princeton University. She is a member of IEEE, ASME, SPIE, and AIAA. She is the recipient of the ONR young investigator award (2004), the NSF CAREER award (2005), and the PECASE award (2006). She can be contacted at Duke University, Department of Mechanical Engineering and Materials Science, Box 90300, Durham, NC 27708-0005 USA.

PY - 2008

Y1 - 2008

N2 - The Business Process Management System (BPMS) has received more attention as companies increasingly realize the importance of business processes. However, traditional BPMS focuses mainly on correct modeling and exact automation of the process flow and pays little attention to the achievement of the final goals of improving process efficiency and process innovation. During and after execution of processes, BPMS usually generates much process log data in which numerous meaningful rules and patterns are hidden. In the present study we employ a data mining technique to extract useful knowledge from the complex process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and of the framework.

AB - The Business Process Management System (BPMS) has received more attention as companies increasingly realize the importance of business processes. However, traditional BPMS focuses mainly on correct modeling and exact automation of the process flow and pays little attention to the achievement of the final goals of improving process efficiency and process innovation. During and after execution of processes, BPMS usually generates much process log data in which numerous meaningful rules and patterns are hidden. In the present study we employ a data mining technique to extract useful knowledge from the complex process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and of the framework.

KW - Business process

KW - Business process mining

KW - Decision tree

KW - Process pattern

UR - http://www.scopus.com/inward/record.url?scp=63849298986&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:63849298986

SP - 1037

EP - 1042

T2 - IIE Annual Conference and Expo 2008

Y2 - 17 May 2008 through 21 May 2008

ER -

A new framework for business process knowledge discovery

Abstract

Other

Keywords

ASJC Scopus subject areas

UN SDGs

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