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On Lifecycle Constraints of Artifact-Centric Workflows

  • Esra Kucukoguz
  • Jianwen Su
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6551)

Abstract

Data plays a fundamental role in modeling and management of business processes and workflows. Among the recent “data-aware” workflow models, artifact-centric models are particularly interesting. (Business) artifacts are the key data entities that are used in workflows and can reflect both the business logic and the execution states of a running workflow. The notion of artifacts succinctly captures the fluidity aspect of data during workflow executions. However, much of the technical dimension concerning artifacts in workflows is not well understood. In this paper, we study a key concept of an artifact “lifecycle”. In particular, we allow declarative specifications/constraints of artifact lifecycle in the spirit of DecSerFlow, and formulate the notion of lifecycle as the set of all possible paths an artifact can navigate through. We investigate two technical problems: (Compliance) does a given workflow (schema) contain only lifecycle allowed by a constraint? And (automated construction) from a given lifecycle specification (constraint), is it possible to construct a “compliant” workflow? The study is based on a new formal variant of artifact-centric workflow model called “ArtiNets” and two classes of lifecycle constraints named “regular” and “counting” constraints. We present a range of technical results concerning compliance and automated construction, including: (1) compliance is decidable when workflow is atomic or constraints are regular, (2) for each constraint, we can always construct a workflow that satisfies the constraint, and (3) sufficient conditions where atomic workflows can be constructed.

Keywords

Business Process Regular Language Business Process Management Business Process Model Cardinality Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Esra Kucukoguz
    • 1
  • Jianwen Su
    • 1
  1. 1.Department of Computer ScienceUniversity of CaliforniaSanta BarbaraUSA

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