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DRain: An Engine for Quality-of-Result Driven Process-Based Data Analytics

Aitor Murguzur, Johannes M. Schleicher, Hong-Linh Truong, Salvador Trujillo, Schahram Dustdar

12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014. Proceedings, pp 349-356


The analysis of massive amounts of diverse data provided by large cities, combined with the requirements from multiple domain experts and users, is becoming a challenging trend. Although current process-based solutions rise in data awareness, there is less coverage of approaches dealing with the Quality-of-Result (QoR) to assist data analytics in distributed data-intensive environments. In this paper, we present the fundamental building blocks of a framework for enabling process selection and configuration through user-defined QoR at runtime. These building blocks form the basis to support modeling, execution and configuration of data-aware process variants in order to perform analytics. They can be integrated with different underlying APIs, promoting abstraction, QoR-driven data interaction and configuration. Finally, we carry out a preliminary evaluation on the URBEM scenario, concluding that our framework spends little time on QoR-driven selection and configuration of data-aware processes.

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