Here, an existing process model is compared with an event log of the same process. The second type of process mining is conformance. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log.Ģ. A discovery technique takes an event log and produces a process model without using any a-priori information. The first type of process mining is discovery. The course covers the three main types of process mining.ġ. The course is at an introductory level with various practical assignments. Then the course focuses on process mining as a bridge between data mining and business process modeling. This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains. Various other process analysis techniques that use event data will be presented. These can be used to automatically learn process models from raw event data. Participants will learn various process discovery algorithms. The course explains the key analysis techniques in process mining. Hence, we refer to this as "data science in action". All of these applications have in common that dynamic behavior needs to be related to process models. Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. The data scientist also needs to relate data to process analysis. It is not sufficient to focus on data storage and data analysis.
DEPENDENCY GRAPH ONLINE SOFTWARE
Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.ĭata science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. Process mining is the missing link between model-based process analysis and data-oriented analysis techniques.