=Paper=
{{Paper
|id=Vol-2400/paper-02
|storemode=property
|title=Analysing Event Data through Process Mining
|pdfUrl=https://ceur-ws.org/Vol-2400/paper-02.pdf
|volume=Vol-2400
|authors=Andrea Marrella
|dblpUrl=https://dblp.org/rec/conf/sebd/Marrella19
}}
==Analysing Event Data through Process Mining==
<pdf width="1500px">https://ceur-ws.org/Vol-2400/paper-02.pdf</pdf>
<pre>
 Analysing Event Data through Process Mining

                                  Andrea Marrella

                     DIAG, Sapienza University of Rome, Italy
                          marrella@diag.uniroma1.it



Extended Abstract
Most organizations create business processes, which are sometimes difficult to
control and comprehend. Understanding these processes is however an absolute
prerequisite prior to taking on any improvement initiative. Process mining pro-
vides a new perspective that makes easier and faster to get a complete and ob-
jective picture of business processes to better control and continuously improve
them, by reducing their costs, production time and risks. This is made possi-
ble by analysing vast quantities of event data available in today’s information
systems. Mainly, which activities are performed, when, and by whom.
    In that sense, process mining sits between computational intelligence and
data mining on the one hand, and business process management on the other
hand. The reference framework for process mining focuses on: (i) conceptual
models describing processes, organizational structures, and the corresponding
relevant data; and (ii) the real execution of processes, as reflected by the foot-
print of reality logged and stored by the information systems in use within an
enterprise. For process mining to be applicable, such information has to be struc-
tured in the form of explicit event logs. In fact, all process mining techniques
assume that it is possible to record the sequencing of relevant events occurred
within an enterprise, such that each event refers to an activity (i.e., a well-defined
step in some process) and is related to a particular case
    Through process mining, decision makers can discover process models from
event logs (process discovery), compare expected and actual behaviors (confor-
mance checking), and enrich models with key information about their actual ex-
ecution (process enhancement). This, in turn, provides the basis to understand,
maintain, and enhance processes based on reality.
    In this tutorial, we introduce the process mining framework, the main process
mining techniques and tools, and the different phases of event data analysis
through process mining, discussing the various ways data and process analysts
can make use of the mined models. Finally, we discuss common pitfalls and
critical issues, and give suggestions on how to mitigate them.



  Copyright c 2019 for the individual papers by the papers authors. Copying permit-
  ted for private and academic purposes. This volume is published and copyrighted by
  its editors. SEBD 2019, June 16-19, 2019, Castiglione della Pescaia, Italy.

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