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

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

In Business Process Management, vol. 8659, pp. 349-356, 2014.

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.

doi:10.1007/978-3-319-10172-9_22    PDF Download