Workflow Data Guards - Semantic Scholar

© Springer Verlag 2005, http://www.springer.de/comp/lncs/index.html Eder, J. & Lehmann, M. (2005). Workflow Data Guards. On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE, Agia Napa, Cyprus, LNCS 3760, pp. 502-519

Workflow Data Guards Johann Eder and Marek Lehmann University of Klagenfurt, Dep. of Informatics-Systems {eder, marek}@isys.uni-klu.ac.at

Abstract. Workflow management systems (WfMSs) frequently use data to coordinate the execution of workflow instances. A WfMS evaluates conditions defined on data to make the control flow decisions i.e. selecting the next activity or deciding on an actor. However, data - within and outside of a running workflow instance - may change dynamically. Modifications of data needed for past control flow decisions may invalidate these decisions. We analyze the desired synchronization policies, and propose a mechanism called data guard to selectively guarantee that significant changes in data are recognized and handled by the data management system to ensure correctness of workflow execution in face of asynchronous updates.

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Introduction

Workflow systems frequently integrate autonomous information systems, using and manipulating data from various heterogenous sources. These data are crucial for the correctness of a workflow execution, because control decisions depend on them. These data can be dynamically modified within and outside of an active workflow instance. Unfortunately, this is frequently ignored and replaced by an assumption that only workflows are accessing data and that the data modified by one workflow are not accessed by another one [1]. The modifications of data can have different impacts on a running workflow instance. We can classify the effects of changes as follow: – The changes have no effect (e.g. modifications concern data already used in the workflow and not needed anymore) – The running instance uses the new values (e.g. contact a customer using his or her new phone number). – The running instance needs the old values (e.g. when the cancellation policy at the time of contracting is relevant, not the latest version.) – The change invalidates parts of the workflow and leads to an exception handling (e.g. a control flow decision based on a data value made during the workflow execution can be invalidated if this data value changes after making the decision.) To illustrate the last case we use a simplified car insurance claim handling workflow definition as modelled in Fig. 1. A possible instance of this workflow is R. Meersman and Z. Tari (Eds.): CoopIS/DOA/ODBASE 2005, LNCS 3760, pp. 502–519, 2005. c Springer-Verlag Berlin Heidelberg 2005


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