Interacting Business Processes based on Data-Driven Service Composition
- Objective: By combining techniques from business process management, database schema matching, and machine learning feedback utilization, this research project aims at providing an important milestone in deriving business process implementations from annotated process models. In particular, data schema matching and machine learning algorithms are used to investigate the impact of the process data on selecting the most appropriate services from a set of available services for implementation of a single business process activities. Further, the relationship of the output data of one process to the input data of another process during communication between interacting processes is investigated, so that data transformation based on a determined data mapping can be used to let the processes interact properly.
- Timeframe: July 2012 to June 2015
- Sponsor: DFG - Deutsche Forschungsgemeinschaft
- Persons involved from BPT: Mathias Weske
- Project partners: Al-Quds University, Jerusalem, Palestinian Territories, and Technion, Faculty of Industrial Engineering & Management, Haifa, Israel