Authors:
Andrea Delgado
and
Daniel Calegari
Affiliation:
Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Montevideo, 11300, Uruguay
Keyword(s):
Process Mining, Data Science, Process and Organizational Data Integration, Process Improvement.
Abstract:
Business Process execution analysis is crucial for organizations to evaluate and improve them. Process mining provides the means to do so, but several challenges arise when dealing with data extraction and integration. Most scenarios consider implicit processes in support systems, with the process and organizational data being analyzed separately. Nowadays, many organizations increasingly integrate process-oriented support systems, such as BPMS, where process data execution is registered within the process engine database and organizational data in distributed potentially heterogeneous databases. They can follow the relational model or NoSQL ones, and organizational data can come from different systems, services, social media, or several other sources. Then, process and organizational data must be integrated to be used as input for process mining tasks and provide a complete view of the operation to detect and make improvements. In this paper, we extend previous work to support the c
ollection of process and organizational data from heterogeneous sources, the integration of these data, and the automated generation of XES event logs to be used as input for process mining.
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