After over 10 years of research, process mining has now developed into a mature research field with an established research community and a strong presence (in terms of publications) in top information systems conferences such as BPM and CAiSE. Aside from a clear presence in terms of publications, process mining courses are taught around the world, books are available, and dedicated process mining MOOCs have been created. For example, the Coursera MOOC “Process Mining: Data Science in Action” had well over 100.000 participants.
These developments in research and teaching have been mirrored by a strong industry uptake, mainly in Europe but now also spreading to other continents. For example, Gartner released a market guide on process mining in April this year, describing the various categories of process mining techniques and how these are supported by various software vendors, while MarketsAndMarkets has predicted that the process analytics market will be worth USD 1,422 million by 2023, with conformance checking (a category of process mining techniques) expected to be the fastest-growing segment of process analytics market during the forecast period.
Process mining is an innovative research field which focusses on extracting business process insights from transactional data commonly recorded by IT systems, with the ultimate goal of analyzing and improving organizational productivity along performance dimensions such as efficiency, quality, compliance and risk. By relying on data rather than perceptions gained from interviews and workshops, process mining shifts the way of thinking from “confidence-based” to “evidence-based” business process management. Thus, process mining distinguishes itself within the information systems domain by its fundamental, evidence-based focus on understanding, analyzing, and improving business processes.
Compared with other data-driven research areas such as machine learning or data mining, process mining differs in the fundamental assumptions that data is generated in the context of more or less structured processes, and that the data contains explicit references to instances of these processes. Another key difference with other data-analysis techniques is that analysis results have to be explained in the context of these (interacting) processes.
Automated discovery of process models
Construction of event logs
Improving quality of event logs
Decision mining for processes
Mining from non-process-aware systems / event streams
Multi-perspective process mining
Simulation/optimization and process mining
Predictive process analytics
Prescriptive process analytics and recommender systems
Privacy, security and ethics
Process model repair
Process performance mining
Process mining quality measures
Variants/deviance analysis and root-cause analysis
Visual process analytics
Business Activity Monitoring and Business Intelligence
Business Process Management
Operations Management and Lean Six Sigma
Process Performance Measurement
Robotic Process Automation (RPA)
Sensors, Internet-of-Things (IoT) and wearable devices
Specific domains such as accounting, finance, government, healthcare, manufacturing