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    ABSTRACT: The Petri Net Markup Language (PNML) is an XML-based interchange format for Petri nets. In order to support different versions of Petri nets and, in particular, future versions of Petri nets, PNML allows the definition of Petri net types. Due to this flexibility, PNML is a starting point for a standard interchange format for Petri nets. This paper discusses the design principles, the basic concepts, and the underlying XML technology of PNML. The main purpose of this paper is to disseminate the ideas of PNML and to stimulate discussion on and contributions to a standard Petri net interchange format.
    Applications and Theory of Petri Nets 2003, 24th International Conference, ICATPN 2003, Eindhoven, The Netherlands, June 23-27, 2003, Proceedings; 01/2003
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    ABSTRACT: The aims of this paper are to identify the issues and forces that were the impetus for two recent developments in academic medicine, evidence-based medicine (EBM) and medical decision making (MDM); to make explicit their underlying similarities and differences; and to relate them to the fates of these innovations. Both developments respond to concerns about practice variation; the rapid growth of medical technology, leading to a proliferation of diagnostic and treatment options; the patient empowerment movement; and psychological research that raised questions about the quality of human judgment and decision making. Their commonalities include: use of Bayesian principles in diagnostic reasoning, and the common structure embedded in an answerable clinical question and a decision tree. Major differences include: emphasis on knowledge or judgment as the fundamental problem; the status of formal models and utility assessment; and the spirit and tone of the innovation. These differences have led to broader acceptance of EBM within academic medicine, while decision analysis, the fundamental tool of MDM, has been less welcomed in clinical circles and has found its place in guideline development, cost-effectiveness analysis, and health policy.
    Inflammation Research 09/2004; 53 Suppl 2(S2):S184-9. DOI:10.1007/s00011-004-0357-2 · 2.35 Impact Factor
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    ABSTRACT: Workflows are formal specifications of processes. In real life, workflows are very hard to manually specify. Groups of experts are usually needed, and the results are influenced by human perceptions. To solve these problems, workflows can be automatically learned from samples of execution processes. This idea is called Workflow Mining. Workflow Mining is usually performed using Petri Nets, which is a good approach for specifying workflows thanks to their high expressiveness. However, learning Petri nets is a very hard task because of their high complexity. This paper presents a new workflow mining algorithm called PALIA. This algorithm is able to learn workflows from parallel activity-based samples of processes using a simpler representation model called the Timed Parallel Automaton (TPA). In addition, a preliminary experiment to evaluate this proposal is also presented.