Process Mining: Fundamentals and Applications

By Fouad Sabry

What Is Process Mining

Process mining is a collection of approaches that relates the fields of data science and process management to support the study of operational processes based on event logs. These techniques were developed to help companies improve their business processes. The objective of process mining is to derive insights and take appropriate action from event data. The availability of event data and the aspiration to achieve process improvement are the driving forces behind process mining, which is an essential component of data science. The approaches of process mining make use of event data in order to demonstrate what individuals, machines, and organizations are actually doing. Process mining gives fresh insights that may be utilized to determine the execution paths taken by operational processes and address the performance and compliance concerns that are caused by these processes.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Process Mining

Chapter 2: Workflow

Chapter 3: Event-Driven Process Chain

Chapter 4: Business Process Management

Chapter 5: Sequential Pattern Mining

Chapter 6: Business Process Discovery

Chapter 7: Alpha Algorithm

Chapter 8: Conformance Checking

Chapter 9: Decision Mining

Chapter 10: Artifact-Centric Business Process Model

(II) Answering the public top questions about process mining.

(III) Real world examples for the usage of process mining in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of process mining' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of process mining.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 5, 2023

Book preview

Chapter 1: Process mining

Process mining is a set of tools for analyzing operational processes using event logs, and it bridges the gap between data science and process management. The purpose of process mining is to get useful insights and take appropriate actions from event data. The availability of event data and the need to enhance processes drive process mining, an essential element of data science. Event data is used in process mining to reveal the true actions of humans, software, and businesses. The execution pathways of operational processes may be identified, and issues with performance and compliance can be addressed, with the help of process mining.

Event data is the foundation for process mining. A process mining tool's input is an event log. One perspective on a process is captured in an event log. Each entry in the log has to have the following three pieces of information: (1) a case id, which is a unique identifier for a specific instance of the process, (2) an activity, which is a description of the event itself, and (3) a date. Additional event characteristics describing resources, expenses, etc., are possible but not required. Any information system supporting operational procedures may be mined for such information with sufficient effort. Various questions about processes may be answered by analyzing this event data, which is what process mining does. made use of.

In cases when a formal description of the process cannot be gained via other means, or if the quality of existing documentation is in dispute, process mining methods are often used.

Interest in enabling diagnostic functions within the framework of Business Process Management technology is shown by recent developments in management disciplines like Business Activity Monitoring (BAM), Business Operations Management (BOM), and Business Process Intelligence (BPI) (e.g., Workflow Management Systems and other process-aware information systems). Process mining is distinct from conventional AI, machine learning, and data mining. In process mining, for instance, methods like process discovery seek to unearth full-fledged process models that may characterize sequential, choice-relational, concurrent, and looping behavior. Methods for ensuring conformity are more akin to optimization than they are to more conventional forms of learning. Nonetheless, machine learning, data mining, and AI may all be generated by process mining. Basic supervised and unsupervised learning tasks may be generated after the discovery of a process model and the alignment of the event log. Foreseeing how much time is left in the processing of an open case or tracking out the sources of compliance issues are two examples.

In October 2009, the IEEE Computational Intelligence Society formed the IEEE Task Force on Process Mining. The mission of this vendor-neutral group is to advance process mining as a field of study and practice by, among other things: raising awareness of current developments in the field among end-users, developers, consultants, and researchers; encouraging the adoption of process mining methods and tools; fostering the creation of novel applications; contributing to the standardization of event data logging formats (such as XES); and holding conferences, seminars, workshops, competitions, panels, and tutorials. The Process Mining Manifesto and the International Conference on Process Mining (ICPM) series were both created by the IEEE Task Force on Process Mining and have been translated into 16 languages.

Wil van der Aalst, a Dutch computer scientist, first used the phrase Process mining in a study proposal he wrote. In 1999, researchers at the University of Eindhoven started exploring a new area of study that drew upon methods from data science and process science. In the past, process mining and workflow management approaches were often confused with one another. Alpha miner was the first realistically useful algorithm for process discovery, and it was created in the year 2000. 2001 saw the introduction of a heuristic-based technique, Heuristic miner, which was remarkably similar. Later on in the chain, more advanced algorithms like inductive miner were created to aid in the finding of processes. Conformance checking quickly became an essential aspect of the developing area of process mining. In 2004, Token-based replay was created to verify compliance with standards. Performance analysis, Decision mining, and Organizational mining were all developed as offshoots of process mining in 2005 and 2006, with the original methodologies of process discovery and compliance testing still in use. In 2007, the pioneering Futura Pi commercial process mining firm was founded. In 2009, an authoritative authority known as the "IEEE