An adaptive work distribution mechanism based on reinforcement learning
Expert Systems with Applications: An International Journal
Reinforcement learning based resource allocation in business process management
Data & Knowledge Engineering
Strong non-leak guarantees for workflow models
Proceedings of the 2011 ACM Symposium on Applied Computing
InDico: information flow analysis of business processes for confidentiality requirements
STM'10 Proceedings of the 6th international conference on Security and trust management
Resource behavior measure and application in business process management
Expert Systems with Applications: An International Journal
Business independent model of mobile workforce management
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Workflow resource pattern modelling and visualization
ACSC '13 Proceedings of the Thirty-Sixth Australasian Computer Science Conference - Volume 135
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Workflow management systems support business processes and are driven by their models. These models cover different perspectives including the control-flow, resource, and data perspectives. This paper focuses on the resource perspective, i.e., the way the system distributes work based on the structure of the organization and capabilities/qualifications of people. Contemporary workflow management systems offer a wide variety of mechanisms to support the resource perspective. Because the resource perspective is essential for the applicability of such systems, it is important to better understand the mechanisms and their interactions. Our goal is not to evaluate and compare what different systems do, but to understand how they do it. We use Colored Petri Nets (CPNs) to model work distribution mechanisms. First, we provide a basic model that can be seen as a reference model of existing workflow management systems. This model is then extended for three specific systems (Staffware, FileNet, and FLOWer). Moreover, we show how more advanced work distribution mechanisms, referred to as resource patterns, can be modelled and analyzed.