Detail Process Mining Based on Clustering: A Quest for Precision Bibliografi Author: Medeiros, Ana Karla Alves de ; Aalst, Wil M.P. van der ; Dongen, Boudewijn F. van ; Greco, Gianluigi ; Guzzo, Antonella ; Sacca, Domenico ; Weijters, A.J.M.M. Topik: Process Discovery; Process Mining; Workflow Mining; Disjunctive Workflow Schema; ProM Framework Bahasa: (EN )     Penerbit: Springer-Verlag Berlin Heidelberg     Tempat Terbit: Heidelberg    Tahun Terbit: 2008     Jenis: Papers/Makalah Fulltext: Process Mining Based on Clustering.pdf (524.7KB; 0 download) Abstract Process mining techniques attempt to extract non-trivial and useful information from event logs recorded by information systems. For example, there are many process mining techniques to automatically discover a process model based on some event log. Most of these algorithms perform well on structured processes with little disturbances. However, in reality it is difficult to determine the scope of a process and typically there are all kinds of disturbances. As a result, process mining techniques produce spaghetti-like models that are difficult to read and that attempt to merge unrelated cases. To address these problems, we use an approach where the event log is clustered iteratively such that each of the resulting clusters corresponds to a coherent set of cases that can be adequately represented by a process model. The approach allows for different clustering and process discovery algorithms. In this paper, we provide a particular clustering algorithm that avoids over-generalization and a process discovery algorithm that is much more robust than the algorithms described in literature [1]. The whole approach has been implemented in ProM. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Lihat Sejarah Pengadaan  Konversi Metadata   Kembali

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