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Preface

Process mining provides a new means to improve processes in a variety of applica-
tion domains. There are two main drivers for this new technology. On the one hand,
more and more events are being recorded thus providing detailed information about
the history of processes. Despite the omnipresence of event data, most organizations
diagnose problems based on fiction rather than facts. On the other hand, vendors of
Business Process Management (BPM) and Business Intelligence (BI) software have
been promising miracles. Although BPM and BI technologies received lots of atten-
tion, they did not live up to the expectations raised by academics, consultants, and
software vendors.
Process mining is an emerging discipline providing comprehensive sets of tools
to provide fact-based insights and to support process improvements. This new disci-
pline builds on process model-driven approaches and data mining. However, process
mining is much more than an amalgamation of existing approaches. For example,
existing data mining techniques are too data-centric to provide a comprehensive un-
derstanding of the end-to-end processes in an organization. BI tools focus on sim-
ple dashboards and reporting rather than clear-cut business process insights. BPM
suites heavily rely on experts modeling idealized to-be processes and do not help
the stakeholders to understand the as-is processes.
This book presents a range of process mining techniques that help organizations
to uncover their actual business processes. Process mining is not limited to pro-
cess discovery. By tightly coupling event data and process models, it is possible to
check conformance, detect deviations, predict delays, support decision making, and
recommend process redesigns. Process mining breathes life into otherwise static
process models and puts today’s massive data volumes in a process context. Hence,
managements trends related to process improvement (e.g., Six Sigma, TQM, CPI,
and CPM) and compliance (SOX, BAM, etc.) can benefit from process mining.
Process mining, as described in this book, emerged in the last decade [102, 106].
However, the roots date back about half a century. For example, Anil Nerode pre-
sented an approach to synthesize finite-state machines from example traces in 1958
[71], Carl Adam Petri introduced the first modeling language adequately capturing
concurrency in 1962 [73], and Mark Gold was the first to systematically explore
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