Process Knowledge in Times of Big Data: From Event Logs to Knowledge-intensive Process Models Through Process Mining

  • Process models are often used as a Knowledge Management method, because these are able to store, visualize, and distribute knowledge within an organization. These knowledge-intensive processes can have a flexible, unstructured form, which is often hard to represent within a process model. The already available approaches of process-oriented Knowledge Management methods, which are considered for Business Process Management, are conducted manually and can therefore become time-consuming and labor-intensive. In addition, in times of Big Data, it is even more challenging to consider all possible cases. Due to the possibility of automatically generating process models through Process Mining, there is a huge potential for creating these processes with real information from event data. This study identifies the possibilities of generating knowledge-intensive process models through Process Mining. At the same time, it addresses the issue of the lack of representation of discovered flexible process models. Although few articles already appeal to the challenge of generating knowledge-intensive processes through Process Mining, the current main focus lies within the operational support in the information systems. The research question of this study is whether actual Process Mining approaches are able to generate process models that are as informative as the models created by processoriented Knowledge Management methods in their current state. To answer the research question, a comprehensive literature review as well as expert interviews have been conducted. Both approaches are part of the design science research methodology, which has been followed during the whole study. The theoretical results have been compared to the practical insights gained from the interviews. As a result, an informal, textual, best practice specification for mining knowledge-intensive process models within a Process Mining project has been developed. This should be considered for implementing an Enterprise Knowledge Medium into the already available IT infrastructure, so that process knowledge can be effectively saved and leveraged through Process Mining.

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Metadaten
Author:Linda Fitas
Referee:Roland Müller
Advisor:Sven Pohland
Document Type:Master's Thesis
Language:English
Date of first Publication:2019/09/05
Publishing Institution:Hochschulbibliothek HWR Berlin
Granting Institution:Hochschule für Wirtschaft und Recht Berlin
Date of final exam:2018/01/25
Release Date:2019/09/05
Page Number:62
Institutes:FB I - Wirtschaftswissenschaften / Business Intelligence and Process Management M.Sc.
Licence (German):License LogoUrheberrechtsschutz