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Titlebook: Data-Driven Process Discovery and Analysis; Third IFIP WG 2.6, 2 Paolo Ceravolo,Rafael Accorsi,Philippe Cudre-Mauro Conference proceedings

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Adriana Berlingieri,Premilla D’Cruzata analysis techniques, such as data mining, are difficult to apply due to the fact that (i) a great number of data mining algorithms can be applied to solve the same problem, and (ii) correctly applying data mining techniques always requires dealing with the inherent features of the data source. T
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Paul L. Kornblith,Michael Walkerning techniques are supported in this framework proposing a running example featured in a Data Loss scenario. We then provide some lesson learned that can be generalized in similar contexts. In particular, we show how applying an appropriate Semantic Lifting to the log may help to discover behaviora
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https://doi.org/10.1007/978-1-4613-2061-6arn a process model (e.g., a Petri net or BPMN model). Next to the automated discovery of the real underlying process, there are process mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users
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978-3-662-46435-9IFIP International Federation for Information Processing 2015
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Towards Collecting Sustainability Data in Supply Chains with Flexible Data Collection Processes,ble products. This involves the reporting and publishing of various sustainability indicators. However, the demands of legal entities and customers constitute a tremendous challenge as products in these domains comprise various components and sub-components provided by suppliers. Hence, sustainabili
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Handling Environment for Publicly Posted Composite Documents, multi-part document format retaining parts in their original formatting for usability, user convenience and information availability, while providing document security and built-in access control for inter- and intra-organizational document workflows distributed over low security channels. Developi
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