Process querying: Enabling business intelligence through query-based process analytics

Polyvyanyy, Artem, Ouyang, Chun, Barros, Alistair P., & van der Aalst, Wil M.P. (2017) Process querying: Enabling business intelligence through query-based process analytics. Decision Support Systems. (In Press)

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Abstract

Highlights

  • A framework for designing process querying methods is proposed

  • The framework is positioned for broader Process Analytics and Business Intelligence

  • The framework is grounded in use cases from the Business Process Management field

  • The framework is informed by and validated via a systematic literature review

  • The framework structures the state of the art and points to gaps in existing research

Abstract

The volume of process-related data is growing rapidly: more and more business operations are being supported and monitored by information systems. Industry 4.0 and the corresponding industrial Internet of Things are about to generate new waves of process-related data, next to the abundance of event data already present in enterprise systems. However, organizations often fail to convert such data into strategic and tactical intelligence. This is due to the lack of dedicated technologies that are tailored to effectively manage the information on processes encoded in process models and process execution records. Process-related information is a core organizational asset which requires dedicated analytics to unlock its full potential. This paper proposes a framework for devising process querying methods, i.e., techniques for the (automated) management of repositories of designed and executed processes, as well as models that describe relationships between processes. The framework is composed of generic components that can be configured to create a range of process querying methods. The motivation for the framework stems from use cases in the field of Business Process Management. The design of the framework is informed by and validated via a systematic literature review. The framework structures the state of the art and points to gaps in existing research. Process querying methods need to address these gaps to better support strategic decision-making and provide the next generation of Business Intelligence platforms.

Impact and interest:

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ID Code: 106408
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Process querying, Process management, Process analytics, Process intelligence, Process science, Business intelligence
DOI: 10.1016/j.dss.2017.04.011
ISSN: 0167-9236
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Facilities: Science and Engineering Centre
Deposited On: 03 May 2017 04:40
Last Modified: 11 Jun 2017 05:46

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