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Process mining provides a new means to improve processes in a variety of application domains. Driven by the omnipresence of event data and the limitations of Business Process Management (BPM) and Business Intelligence (BI) approaches, a new discipline has emerged that builds on classical process model-driven approaches and data mining. During the last decade, breakthroughs have been realized that make it possible the automatically discover business processes from event data present in information systems ranging from ERP systems (e.g. SAP and Oracle) and E-business applications to hospital information systems and high-tech production systems. Process mining can also be used for conformance checking in the context of auditing, compliance, and governance. Moreover, by projecting event data onto discovered models, business processes can be improved in terms of costs, time, and quality.
The book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" (Springer, http://springer.com/978-3-642-19344-6, ISBN 978-3-642-19344-6) by Wil van der Aalst is the first book on Process Mining. It collects the state-of-the-art results available in publications, software and best practices. To support the book a website http://www.processmining.org/book/ has been created containing slides, event logs, and models. The slides can be used for presentations, e.g., for university seminars to discuss the emerging topic of process mining and for training IT and business consultants to provide services based on process mining. Many of the event logs mentioned in the book are available via http://www.processmining.org/book/. The open-source software ProM (http://www.promtools.org/prom6/) and other tools supporting the XES or MXML format can be used to discover process models from these example logs. Also models have been included to experiment with conformance checking (pinpoint discrepancies between event log and model) and other types of analysis.