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Business Processes Compliance in Partially Observable Environments

EasyChair Preprint 837, version 1

Versions: 12history
16 pagesDate: March 17, 2019

Abstract

This paper aims to provide an answer: how to obtained compliance through observation and control in instances of business processes modelled on Business Process Management(BPM), in partially observable environment. An organization is a dynamic system where actors play roles and produce results and decisions autonomously, changing the overall state of the system. These decisions often occur in environments that are not fully observable. In order to face with the market demand and legal impositions, organizations need to come up with innovative solutions by optimizing their business transaction models allowing them to assist in decision-making processes. The business process models are intended to represent an organizational reality and restrict the freedom of design to allow common understanding between stakeholders and to define the roles of the actors, who instantiate the state transactions of business process. Organizations need to ensure that operational processes are performed in a controlled way to meet predefined requirements, complying with regulations, laws and agreements established between internal and external stakeholders. This project concerns the beginning of a proposal to master this problem. The solution was implemented in enterprise simulation environment, using Enterprise Cartography(EC). The results obtained demonstrated the ability to observe and control the process instances as a contribution to improving the compliance of Business Process, modelled in BPMN.

Keyphrases: Business Process Models, Compliance, Development process., Enterprise Cartography

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:837,
  author    = {Isabel Silva and Pedro Sousa and Sérgio Guerreiro},
  title     = {Business Processes Compliance in Partially Observable Environments},
  doi       = {10.29007/m4db},
  howpublished = {EasyChair Preprint 837},
  year      = {EasyChair, 2019}}
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