Umsetzung eines Systems zur Prozessoptimierung im Recruiting basierend auf einem Process-Cube

Process mining makes it possible to discover, check or enhance processes from event data. These event data are generated from activities performed by humans, machines or software and enables the derivation of process models. The research area of process mining establishes the connection between data mining and process modeling. In general, the application of process mining promises good results especially for a well-standardized process with few variants. The use of process cubes makes it possible to achieve comparability with regard to different variants of a process. The principles of process mining are also applied in the conception of process cubes and are extended by multidimensional aspects. For this purpose, the relevant analysis dimensions are linked with the event data and processed in a multidimensional process cube. The structure of the data model is based on the multidimensional data model, which is the central modeling paradigm in data warehousing. In this thesis, the implementation of a system for process optimization in recruiting at the company Epunkt, with a process cube as a central component, based on the approaches described in the literature, is demonstrated. Due to the different heterogeneous processes in recruiting, the process cube can be used to analyze and compare the different variants of the recruiting process. This should enable the collection of process knowledge in relation to different analysis dimensions such as location, team or job domain. The designed system should help the company Epunkt to keep an overview of the processes and to be able to control them despite rapid growth. This also includes that the designed system enables a conformity check of the processes with regard to the process specifications.