By Malu Castellanos, Jan Mendling, Barbara Weber, Ton Weijters (auth.), Arthur ter Hofstede, Boualem Benatallah, Hye-Young Paik (eds.)
This publication constitutes the completely refereed post-workshop lawsuits of 6 internationl workshops held in Brisbane, Australia, at the side of the fifth overseas convention on company method administration, BPM 2007, in September 2007.
The forty five revised complete papers awarded have been conscientiously reviewed and chosen from greater than eighty submissions to the next 6 overseas workshops: enterprise strategy Intelligence (BPI 2007), enterprise strategy layout (BPD 2007), Collaborative enterprise tactics (CBP 2007), Process-oriented info platforms in Healthcare (ProHealth 2007), Reference Modeling (RefMod 2007), and Advances in Semantics for net companies (semantics4ws 2007).
Read Online or Download Business Process Management Workshops: BPM 2007 International Workshops, BPI, BPD, CBP, ProHealth, RefMod, semantics4ws, Brisbane, Australia, September 24, 2007, Revised Selected Papers PDF
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Additional info for Business Process Management Workshops: BPM 2007 International Workshops, BPI, BPD, CBP, ProHealth, RefMod, semantics4ws, Brisbane, Australia, September 24, 2007, Revised Selected Papers
Event and business object descriptions tell us where the information needed to extract their instances exists. Figure 4 shows an meta level description for how business objects, events and their relations can be deﬁned for process mining projects analysing processes related to the creation and chaning of purchase requisitions, purchase orders, and invoice receipts. In some SAP tables, several types of elements are stored in the same table. CDHDR is one such table, where creation and change events for several document types are stored.
The higher its value, the deeper the resulting hierarchy can be. ) Number of features” deﬁnes the dimension of the feature space over which the clustering algorithm is applied. e. ) Number of clusters per split”). ) Number of features” lead to higher quality in the clustering results but it requires more computational time. The window for showing the results has three panels: the hierarchy of the found clusters (cf. Figure 5(c)), the model mined for each cluster2 (cf. Figure 5(d)), and the set of relevant features (cf.
To make use of the raw data material, pre-processing activities are often required before process mining algorithms can be applied. The goal of the pre-processing phase is to extract normalized event logs, a job which involves activities such as data cleansing, feature selection, and merging of distinct data sources. The output of the pre-processing phase is process or event instances that can be explored through graphs and process- and data mining models. The goal of the exploration phase is to give the user a deeper understanding of his business, which again can be exploited to improve organizational structures and policies.