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Chapter 14
            Epilogue

















            To conclude this book, we summarize the main reasons for using process mining.
            Process mining can be seen as the “missing link” between data mining and tradi-
            tional model-driven BPM. Although mature process mining techniques and tools
            are available, several challenges remain to further improve the applicability of the
            techniques presented in the preceding chapters. Therefore, we list the most impor-
            tant challenges. Finally, we encourage the reader to start using process mining today.
            For organizations that store event data in some form, the threshold to get started is
            really low.



            14.1 Process Mining: A Bridge Between Data Mining
                 and Business Process Management


            Process mining is an important tool for modern organizations that need to manage
            nontrivial operational processes. On the one hand, there is an incredible growth of
            event data. On the other hand, processes and information need to be aligned per-
            fectly in order to meet requirements related to compliance, efficiency, and customer
            service. The digital universe and the physical universe are amalgamating into one
            universe where events are recorded as they happen and processes are guided and
            controlled based on event data.
              In Part I, we presented the two main disciplines that process mining is building
            on: Business Process Management (BPM) and data mining. Chapter 2 introduced
            several process modeling techniques and discussed the role of process models in
            the context of BPM. In Chap. 3, we introduced some of the basic data mining tech-
            niques.
              Classical BPM approaches use process models as static descriptions or to drive a
            BPM/WFM system. If process models are just descriptive, they tend to be informal
            and of low quality (i.e., not describing reality well). If models are used to configure
            a BPM/WFM system, they tend to force people to work in a particular manner. Data
            mining techniques aim to describe and understand reality based on historic data.
            W.M.P. van der Aalst, Process Mining,                           337
            DOI 10.1007/978-3-642-19345-3_14, © Springer-Verlag Berlin Heidelberg 2011
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