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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.
