Conference proceeding
Two Dependency Modeling Approaches for Business Process Adaptation
KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, v 5914, pp 418-429
01 Jan 2009
Abstract
Complex business processes in the form of workflows or service compositions are built from individual building blocks, namely activities or services. These building blocks cooperate to achieve the overall goal of the process. In many cases dependencies exist between the individual activities, i.e. the execution of one activity depends on another. Knowledge about dependencies is especially important for the management of the process at runtime in cases where problems occur and the process needs to be adapted. In this paper we present and compare two approaches for modeling dependencies as a base for managing adaptations of complex business processes. Based on two use cases from the domain of workflow management and service engineering we illustrate the need for capturing dependencies and derive the requirements for dependency modeling. For dependency modeling we discuss two alternative solutions. One is based on an OWL-DL ontology and the other is based on a meta-model approach. Although many of the requirements of the use cases are similar, we show that there is no single best solution for a dependency model.
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Details
- Title
- Two Dependency Modeling Approaches for Business Process Adaptation
- Creators
- Christian Sell - SAP Research CEC Dresden, SAP AG, Dresden, Germany 01187Matthias Winkler - SAP Research CEC Dresden, SAP AG, Dresden, Germany 01187Thomas Springer - TU DresdenAlexander Schill - TU Dresden
- Contributors
- D Karagiannis (Editor)Z Jin (Editor)
- Publication Details
- KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, v 5914, pp 418-429
- Series
- Lecture Notes in Artificial Intelligence
- Publisher
- Springer Nature
- Number of pages
- 2
- Grant note
- 01MQ07012 / German Federal Ministry of Economy and Technology
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Biochemistry and Molecular Biology
- Web of Science ID
- WOS:000276636000036
- Scopus ID
- 2-s2.0-77249108352
- Other Identifier
- 991020099589404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems