[Back]

A. Murguzur, J. Schleicher, H. Truong, S. Trujillo, S. Dustdar:
"DRain: An Engine for Quality-of-Result Driven Process-Based Data Analytics (Short Paper)";
Talk: 12th International Conference on Business Process Management (BPM 2014), Eindhoven, The Netherlands; 2014-09-07 - 2014-09-11; in: "Business Process Management, 12th International Conference, BPM 2014, Proceedings", S. Sadiq, P. Soffer, H. Völzer (ed.); Springer, LNCS 8659 (2014), ISBN: 978-3-319-10171-2; 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.

Data-aware Processes, Runtime Configuration, Data Analytics, Smart Cities

http://dx.doi.org/10.1007/978-3-319-10172-9_22