Automated Process Discovery through Process Mining
Process mining is a bridge between data mining and business process modeling.
Process mining is used for Business Process Intelligence.
Putting Data Science into Action – for Process Mining of processes with dynamic behaviour:
Process mining bridges the missing link between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-oriented analysis techniques (e.g., machine learning and data mining). Data science can be applied directly to analyze and improve processes in a variety of domains.
Data science – It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems).
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.
A) Conduct process mining projects in a structured manner
a) Identify data needed to start a process mining project.
b) Characterize the questions that can be answered based on such event data.
B) Process discovery algorithms, approaches and technologies
a) Used to automatically learn process models from raw event data
b) Use event data to support decision making and business process (re)design
c) Relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification
Three main types of process mining.
1) Process discovery: apply process discovery techniques to learn a process model from an event log (both manually and using tools)
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) Conformance: compare event logs and process models (both manually and using tools)
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) Enhancement: extend a process model with information extracted from the event log (e.g., show bottlenecks)
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.
C) Operational Support (prediction and recommendation)
Process mining techniques can be used not only in an offline setting, but also in an online setting.
This 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.
D) Business Process Improvement
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.