This talk addresses how to efficiently process unstructured data for process mining. The volume of data is continuously increasing and the ability and demand to efficiently analyze the data has become even more crucial. Although several suitable techniques and tools already exist to efficiently process and analyze unstructured data, the challenge still exists how to intervene process orientation into unstructured data analysis. This combination promises uncovering new insights in terms of causal effects or bottlenecks in data that could not be directly found with alternative technique. Finally, involving users in such an analytics pipeline gives confidence in decision-making. This talk summarizes challenges, presents use cases, and gives an outlook on prospective research projects for process mining on unstructured data.

Biography:

Agnes Koschmider is a professor of Business Informatics at the University of Bayreuth. Prior to this position Agnes Koschmider was professor of Business Informatics at the Computer Science Institute of the University of Kiel. She completed her PhD and her habilitation in Applied Informatics at KIT.

Her research focuses on methods for data-driven analysis and explanation of processes (process mining) based on artificial intelligence.

At the center of her research is process analytics: developing a pipeline to efficiently process raw data (time series, sensor event data, and video data) to abstract it to process models. The application of such a data pipelines can be found in many disciplines such as medicine, agricultural sciences, material sciences or marine sciences.