Provenance for business events. (25th January 2015)

• Record Type: Journal Article
• Authors: Hammad, Rafat; Wu, Ching-Seh
• Is Part Of: International journal of big data intelligence. Volume 1:Number 4(2014)
• Page Start: 205
• Page End: 214
• Publication Date: 2015-01-25
• Subjects: provenance -- provenance management -- data streams -- real-time monitoring -- distributed event-based systems -- message dependence graph -- root cause analysis; Big data -- Periodicals; 005.705
• Languages: English
• ISSNs: 2053-1389

Abstract

In today's business environment, applications generate massive amounts of business data at various levels of granularity. During execution of business processes, a number of issues may occur, e.g., system failures, process failures, service failures, or human errors, that can result in the processes not executing as expected, and as a result not adhering to the required compliance concerns. Business provenance is an emerging concept which gives the flexibility to capture information required to address a specific compliance or performance goal. This paper discusses the importance of data provenance and presents a framework to capture, model, and persists provenance for business events data. We propose a method to model the business events in such a way that can be used for continuous compliance monitoring and for historical root cause analysis. We present a design of our proposed framework and its components along with a prototype implementation.