be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification, understand the role of Big Data in today’s society, have a good understanding of Business Process Intelligence techniques (in particular process mining), After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field. The course uses many examples using real-life event logs to illustrate the concepts and algorithms. Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development. Process mining provides not only a bridge between data mining and business process management it also helps to address the classical divide between "business" and "IT". Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases. An example is the detection of non-conformance at the moment the deviation actually takes place. The latter is known as operational support. Process mining techniques can be used in an offline, but also online setting. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model.
Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. The third type of process mining is enhancement. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa.ģ. 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 BUILDER 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.
0 kommentar(er)
