Abstract
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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Murguzur, A., Schleicher, J.M., Truong, HL., Trujillo, S., Dustdar, S. (2014). DRain: An Engine for Quality-of-Result Driven Process-Based Data Analytics. In: Sadiq, S., Soffer, P., Völzer, H. (eds) Business Process Management. BPM 2014. Lecture Notes in Computer Science, vol 8659. Springer, Cham. https://doi.org/10.1007/978-3-319-10172-9_22
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DOI: https://doi.org/10.1007/978-3-319-10172-9_22
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10171-2
Online ISBN: 978-3-319-10172-9
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