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Titlebook: Data-Driven Process Discovery and Analysis; 8th IFIP WG 2.6 Inte Paolo Ceravolo,Maurice van Keulen,María Teresa Góm Conference proceedings

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書目名稱Data-Driven Process Discovery and Analysis
副標(biāo)題8th IFIP WG 2.6 Inte
編輯Paolo Ceravolo,Maurice van Keulen,María Teresa Góm
視頻videohttp://file.papertrans.cn/264/263317/263317.mp4
叢書名稱Lecture Notes in Business Information Processing
圖書封面Titlebook: Data-Driven Process Discovery and Analysis; 8th IFIP WG 2.6 Inte Paolo Ceravolo,Maurice van Keulen,María Teresa Góm Conference proceedings
描述.This book constitutes revised selected papers from the 8.th. and 9.th. IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2018, held in Seville, Spain, on December 13–14, 2018, and SIMPDA 2019, held in Bled, Slovenia, on September 8, 2019.?. From 16 submissions received for SIMPDA 2018 and 9 submissions received for SIMPDA 2019, 3 papers each were carefully reviewed and selected for presentation in this volume. They cover theoretical issues related to process representation, discovery, and analysis or provide practical and operational examples of their application..
出版日期Conference proceedings 2020
關(guān)鍵詞BPM; Business Process Management; Data Mining; Process Analysis; Process Mining
版次1
doihttps://doi.org/10.1007/978-3-030-46633-6
isbn_softcover978-3-030-46632-9
isbn_ebook978-3-030-46633-6Series ISSN 1865-1348 Series E-ISSN 1865-1356
issn_series 1865-1348
copyrightIFIP International Federation for Information Processing 2020
The information of publication is updating

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General Model for Tracking Manufacturing Products Using Graph Databases,pplied and mixed to a certain degree during an indeterminate number of stages, what makes it very difficult to trace each of these parts from its origin to its presence in a final product. In order to overcome this limitation, we have worked towards the design of a general solution aiming to improve
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Pau Luque,Ismael Martínez Torrestion of time is spent actually applying techniques to discover, control and predict the business process. Moreover, current process mining techniques assume a . case notion. However, in real-life processes often different case notions are intertwined. For example, events of the same order handling p
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Jorge Luis Fabra-Zamora,Gonzalo Villa Rosase optimized, despite heterogeneous nature of these data structures. This data may also be used for various types of analysis, such as reasoning, process querying and process mining, which consume different data formats. However, all these structures and formats share a common ground: the business-pr
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