Article,

Special Issue on: Knowledge-intensive Business Processes

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Journal on Data Semantics, 4 (1): 1-2 (March 2015)
DOI: 10.1007/s13740-014-0044-6

Abstract

Nowadays, workflow management systems (WfMSs) and process management systems (PMSs) are widely used in all human activities, ranging from classical ones (management of supply chain, postal tracking delivery, etc.) to very dynamic ones (healthcare, emergency management, etc.). Every aspect of a business process involves a certain amount of knowledge that can depend on both the complexity of the domain of interest and the modeling language used to represent the process itself. Some processes behave in a way that is well understood, predictable and repeatable: the tasks are discrete and the control flow is straightforward. Recent discussions indicate the increasing demand for knowledgeintensive processes. A knowledge-intensive process is one in which the people performing the process are involved in a fair degree of uncertainty. This is due to the high number of tasks to be represented and to their unpredictable nature, or to the difficulty in modeling the whole knowledge of the domain at design time. In realistic environments, for example, actors lack important knowledge at execution time or this knowledge can become obsolete during the process' execution. Indeed, even if each actor (at some point) has perfect knowledge of the world, it could not be certain of its beliefs at later time points, since tasks by other actors may change the world without those changes being perceived. Typically, a knowledge-intensive process cannot be modeled to sufficient detail by classical static processmodels and workflows. There is still a lack of maturity in some respect, i.e., a lack of a semantic associated with the models or an easy way to reason about such semantic.

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