Date

2018

Author

Esgin, Eren

Metadata

Show full item record

Item Usage Stats

158
views

82
downloads

Cite This

Business Process Management (BPM) gains growing attention by generic process design and execution capabilities empowered by process-aware information systems. During execution of these transactional information systems, end-users leave traces in the form of event logs, which can be used as a main data source for behavior analysis. Process mining encompasses the techniques for automatically discovering process from these event logs, checking conformance between the reference process model and process executions, as well as analyzing, predicting and enhancing the performance of business processes. With the emergence of new shared economical models and system architectures, monolithic process perspective is evolved through cross-organizational applications. While contemporary information systems provide functionality for process management within the organizations, a systematic approach to support and analyze multi-organizational processes is missing. Cross-organizational process mining supports the use of commonality and collaboration for process configuration. However, this functionality creates the challenge of dealing with variability across organizations. In this study, we propose a three phased cross-organizational process mining framework in order to extract the commonalities among different organizations serving the same business values. While dominant behavior extraction phase initially derives the sequence of tasks expressing the most typical behavior within the process instances, sequence alignment phase measures the degree of similarities between the process candidates by confidence enhanced cost functioning, and depicts the neighborhood among these alternatives in terms of process families. At process configuration phase, common regions that indicate a functional inheritance or abstractions in the process families are visualized at sequence alignment matrices and interpreted by new feature sets, namely identical and maximal identical pair. According to the experimental results, proposed approach presents a viable and robust cost function in incorporating the business context at process similarity measurement and clustering the process alternatives into process families.

Subject Keywords

Process control., System analysis., Business intelligence., Industrial engineering.

URI

http://etd.lib.metu.edu.tr/upload/12622915/index.pdf
https://hdl.handle.net/11511/27909

Collections

Graduate School of Informatics, Thesis