Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. 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.
This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments.
The course covers the three main types of process mining.
1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. 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.
2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa.
3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. 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. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. 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.
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”. Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development.
The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field.
After taking this course you should:
– have a good understanding of Business Process Intelligence techniques (in particular process mining),
– understand the role of Big Data in today’s society,
– be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification,
– be able to apply basic process discovery techniques to learn a process model from an event log (both manually and using tools),
– be able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools),
– be able to extend a process model with information extracted from the event log (e.g., show bottlenecks),
– have a good understanding of the data needed to start a process mining project,
– be able to characterize the questions that can be answered based on such event data,
– explain how process mining can also be used for operational support (prediction and recommendation), and
– be able to conduct process mining projects in a structured manner.