- Objective: The health care is facing major challenges to obtain efficient. Therefore, reproducible and transparent quality of treatment processes must be achieved. PIGE has set itself the goal of achieving this quality through business process intelligence, i.e., by fusion of business intelligence and business process management. This means that execution data (e.g., treatment, waiting or processing times) is connected directly with business processes so that it becomes apparent what processes are responsible for the detected values. Key technical challenge in business process intelligence is the correlation between execution data of information systems and business processes. With regard to the clinical environment, the data from clinical information systems must be brought together with the clinical pathways. The project PIGE refines modeling techniques and researchs novel methods on the conceptual side, whereas on the technical side interfaces to clinical information systems and prototypes are implemented. The aim of the project is to expand the current state of science in the field of process analysis and process improvement, and the prototypical implementation of the developed concepts.
- Timeframe: December 2010 to November 2013
- Sponsor: BMBF - Bundesministerium für Bildung und Forschung
- Link: http://pige-projekt.de
- Persons with major involvement: Nico Herzberg, Mathias Weske
- Objective: GET Service aims at developing a platform for efficient multi-modal transportation planning and execution in a manner that reduces greenhouse gas emissions. This includes the cooperative planning for reduced slack time, insight into owned and chartered resources, as well as quick insight in and response to unexpected events.
- Timeframe: October 2012 to September 2015
- Sponsor: EU's 7th Framework Programme
- Link: http://getservice-project.eu
- Persons involved: Anne Baumgrass, Andreas Meyer, Mathias Weske
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: Oleh Khovalko, Mathias Weske
- Project partners: Al-Quds University, Jerusalem, Palestinian Territories, and Technion, Faculty of Industrial Engineering & Management, Haifa, Israel