Authors: Rafat Hammad; Ching-Seh Wu
Addresses: Department of Computer Science and Engineering, Oakland University, 2200 N Squirrel Rd, Rochester, MI 48309, USA ' Department of Computer Science and Engineering, Oakland University, 2200 N Squirrel Rd, Rochester, MI 48309, USA
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.
Keywords: provenance management; data streams; real-time monitoring; distributed event-based systems; message dependence graph; root cause analysis; business data; business provenance; data provenance; compliance monitoring.
International Journal of Big Data Intelligence, 2014 Vol.1 No.4, pp.205 - 214
Available online: 18 Jan 2015Full-text access for editors Access for subscribers Purchase this article Comment on this article