Data-driven process discovery and an...
Accorsi, Rafael.

 

  • Data-driven process discovery and analysis[electronic resource] :4th International Symposium, SIMPDA 2014, Milan, Italy, November 19-21, 2014 : revised selected papers /
  • 紀錄類型: 書目-語言資料,印刷品 : Monograph/item
    杜威分類號: 006.312
    書名/作者: Data-driven process discovery and analysis : 4th International Symposium, SIMPDA 2014, Milan, Italy, November 19-21, 2014 : revised selected papers // edited by Paolo Ceravolo, Barbara Russo, Rafael Accorsi.
    其他題名: SIMPDA 2014
    其他作者: Ceravolo, Paolo.
    團體作者: Clark Conference
    出版者: Cham : : Springer International Publishing :, 2015.
    面頁冊數: ix, 123 p. : : ill., digital ;; 24 cm.
    Contained By: Springer eBooks
    標題: Data mining
    標題: Management information systems
    標題: Business - Congresses. - Data processing
    標題: Computer Science.
    標題: Data Mining and Knowledge Discovery.
    標題: Business Process Management.
    標題: Information Systems Applications (incl. Internet)
    標題: Computer Appl. in Administrative Data Processing.
    ISBN: 9783319272436
    ISBN: 9783319272429
    內容註: Discovery of Frequent Episodes in Event Logs -- Finding Suitable Activity Clusters for Decomposed Process Discovery -- History-based Construction of Alignments for Conformance Checking: Formalization and Implementation? -- Dynamic Constructs Competition Miner - Occurrence vs. Time-based Ageing -- Trustworthy Cloud Certification: A Model-Based Approach.
    摘要、提要註: This book constitutes the thoroughly refereed proceedings of the Fourth International Symposium on Data-Driven Process Discovery and Analysis held in Riva del Milan, Italy, in November 2014. The five revised full papers were carefully selected from 21 submissions. Following the event, authors were given the opportunity to improve their papers with the insights they gained from the symposium. During this edition, the presentations and discussions frequently focused on the implementation of process mining algorithms in contexts where the analytical process is fed by data streams. The selected papers underline the most relevant challenges identified and propose novel solutions and approaches for their solution.
    電子資源: http://dx.doi.org/10.1007/978-3-319-27243-6
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