Int. J. of Big Data Intelligence   »   2014 Vol.1, No.4

 

 

Title: Provenance for business events

 

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.

 

DOI: 10.1504/IJBDI.2014.066956

 

Int. J. of Big Data Intelligence, 2014 Vol.1, No.4, pp.205 - 214

 

Submission date: 09 Jan 2014
Date of acceptance: 31 May 2014
Available online: 18 Jan 2015

 

 

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