1,028,276 research outputs found

    Ontology-based metrics computation for business process analysis

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    Business Process Management (BPM) aims to support the whole life-cycle necessary to deploy and maintain business processes in organisations. Crucial within the BPM lifecycle is the analysis of deployed processes. Analysing business processes requires computing metrics that can help determining the health of business activities and thus the whole enterprise. However, the degree of automation currently achieved cannot support the level of reactivity and adaptation demanded by businesses. In this paper we argue and show how the use of Semantic Web technologies can increase to an important extent the level of automation for analysing business processes. We present a domain-independent ontological framework for Business Process Analysis (BPA) with support for automatically computing metrics. In particular, we define a set of ontologies for specifying metrics. We describe a domain-independent metrics computation engine that can interpret and compute them. Finally we illustrate and evaluate our approach with a set of general purpose metrics


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    Metrics are a sine qua non condition of an efficient management. Indeed, management cannot be based upon intuition: it has to be underpinned by figures.\ud \ud The choice of the indicators, their link to objectives and their strategic value, the weight to be assigned to them in terms of performance and cascading accountabilities imply considering:\ud \ud - outputs vs outcomes\ud - short term vs long term\ud - suboptimization vs global optimization\ud - lagging vs leaders indicators\ud \ud New metrics methods in management are doomed to give priority to effectiveness over efficiency.\ud \u

    Toric LeBrun metrics and Joyce metrics

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    We show that, on the connected sum of complex projective planes, any toric LeBrun metric can be identified with a Joyce metric admitting a semi-free circle action through an explicit conformal equivalence. A crucial ingredient of the proof is an explicit connection form for toric LeBrun metrics.Comment: 10 page


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    Semantic metrics

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    In the context of the Semantic Web, many ontology-related operations, e.g. ontology ranking, segmentation, alignment, articulation, reuse, evaluation, can be boiled down to one fundamental operation: computing the similarity and?or dissimilarity among ontological entities, and in some cases among ontologies themselves. In this paper, we review standard metrics for computing distance measures and we propose a series of semantic metrics. We give a formal account of semantic metrics drawn from a variety of research disciplines, and enrich them with semantics based on standard Description Logic constructs. We argue that concept-based metrics can be aggregated to produce numeric distances at ontology-level and we speculate on the usability of our ideas through potential areas