Integrating machine learning and workflow management to support acquisition and adaptation of workflow models

Joachim Herbst and Dimitris Karagiannis

Intelligent Systems in Accounting, Finance and Management, 2000, vol. 9, issue 2, 67-92

Abstract: Current workflow management systems (WFMS) offer little aid for the acquisition of workflow models and their adaptation to changing requirements. To support these activities we propose to apply techniques from machine learning, which enable an inductive approach to workflow acquisition and adaptation. We present a machine learning component that combines two different machine learning algorithms: the first induces the structure of sequential workflows and the second is responsible for the induction of transition conditions. The second task can be solved by applying standard decision rule induction algorithms. In this contribution we focus mainly on the algorithms for the first task. For this purpose we describe two algorithms based on the induction of hidden Markov models. The first algorithm is a bottom‐up, specific‐to‐general algorithm and the other applies a top‐down, general‐to‐specific strategy. Both algorithms have been implemented in a research prototype. In six scenarios we evaluate and compare the two algorithms experimentally. The induced workflow models can be imported by the business process management system ADONIS. Copyright © 2000 John Wiley & Sons, Ltd.

Date: 2000
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1002/1099-1174(200006)9:23.0.CO;2-7

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:isacfm:v:9:y:2000:i:2:p:67-92

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1099-1174

Access Statistics for this article

More articles in Intelligent Systems in Accounting, Finance and Management from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().