contributed articles

Doi: 10.1145/2240236.2240257

Using real event data to X-ray business
processes helps ensure conformance
between design and reality.

By WiL VAn DER AALSt Process Mining

recent BreaKthrOuGhS In process mining research make it possible to discover, analyze, and improve business processes based on event data. Activities executed by people, machines, and software leave trails in so-called event logs. What events (such as entering a customer order into SAP, a passenger checking in for a flight, a doctor changing a patient’s dosage, or a planning agency rejecting a building permit) have in common is that all are recorded by information systems. Data volume and storage capacity have grown spectacularly over the past decade, while the digital universe and the physical universe are increasingly aligned. Business processes thus ought to be managed, supported, and improved based on event data rather than on subjective opinions or obsolete experience. Application of process mining in hundreds of organizations worldwide shows that managers and users alike tend to overestimate their knowledge of

their own processes. Process mining results can thus be viewed as X-rays revealing what really goes on inside processes and can be used to diagnose problems and suggest proper treatment. The practical relevance of process mining and related interesting scientific challenges make process mining a hot topic in business process management (BPM). This article offers an introduction to process mining by discussing the core concepts and applications of the emerging technology.

Process mining aims to discover, monitor, and improve real processes by extracting knowledge from event logs readily available in today’s information systems. 1, 2 Although event data is everywhere, management decisions tend to be based on PowerPoint charts, local politics, or management dashboards rather than on careful analysis of event data. The knowledge hidden in event logs cannot be turned into actionable information. Advances in data mining made it possible to find valuable patterns in large datasets and support complex decisions based on the data. However, classical data mining problems (such as classification, clustering, regression, association rule learning, and sequence/ episode mining) are not process-centric. Therefore, BPM approaches tend to resort to handmade models, and process mining research aims to bridge the gap between data mining and BPM. Metaphorically, process

key insights

Although large organizations appreciate the value of big data, they rarely connect event data to process models; process mining represents the missing link between analysis of big data and business process management.

Aligning event data and process models is essential for conformance checking and performance analysis; additionally, relating events to process models helps breathe life into other wise static diagrams.

the exponential growth of event data and the need for smarter, leaner processes motivates use of process mining.