Authors:
Andrea Delgado
;
Daniel Calegari
;
Adriana Marotta
;
Laura González
and
Libertad Tansini
Affiliation:
Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Montevideo, 11300, Uruguay
Keyword(s):
Process Mining, Data Mining, Data Science, Methodology, Organizational Improvement, Business Intelligence.
Abstract:
The socio-technical system supporting an organization’s daily operations is becoming more complex, with distributed infrastructures integrating heterogeneous technologies enacting business processes and connecting devices, people, and data. This situation promotes large amounts of data in heterogeneous sources, both from their business processes and organizational data. Obtaining valuable information and knowledge from this is a challenge to make evidence-based improvements. Process mining and data mining techniques are very well known and have been widely used for many decades now. However, although there are a few methodologies to guide mining efforts, there are still elements that have to be defined and carried out project by project, without much guidance. In previous works, we have presented the PRICED framework, which defines a general strategy supporting mining efforts to provide organizations with evidence-based business intelligence. In this paper, we refine such ideas by pr
esenting a concrete methodology. It defines phases, disciplines, activities, roles, and artifacts needed to provide guidance and support to navigate from getting the execution data, through its integration and quality assessment, to mining and analyzing it to find improvement opportunities.
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