An approach to optimize data processing in business processes

  • Authors:

  • Marko Vrhovnik;Holger Schwarz;Oliver Suhre;Bernhard Mitschang;Volker Markl;Albert Maier;Tobias Kraft

  • Affiliations:

  • University of Stuttgart, Stuttgart, Germany;University of Stuttgart, Stuttgart, Germany;IBM Deutschland, Böblingen, Germany;University of Stuttgart, Stuttgart, Germany;IBM Almaden Research Center, San Jose, CA;IBM Deutschland, Böblingen, Germany;University of Stuttgart, Stuttgart, Germany

  • Venue:
  • VLDB '07 Proceedings of the 33rd international conference on Very large data bases
  • Year:
  • 2007

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Abstract

In order to optimize their revenues and profits, an increasing number of businesses organize their business activities in terms of business processes. Typically, they automate important business tasks by orchestrating a number of applications and data stores. Obviously, the performance of a business process is directly dependent on the efficiency of data access, data processing, and data management. In this paper, we propose a framework for the optimization of data processing in business processes. We introduce a set of rewrite rules that transform a business process in such a way that an improved execution with respect to data management can be achieved without changing the semantics of the original process. These rewrite rules are based on a semi-procedural process graph model that externalizes data dependencies as well as control flow dependencies of a business process. Furthermore, we present a multi-stage control strategy for the optimization process. We illustrate the benefits and opportunities of our approach through a prototype implementation. Our experimental results demonstrate that independent of the underlying database system performance gains of orders of magnitude are achievable by reasoning about data and control in a unified framework.