=Paper=
{{Paper
|id=None
|storemode=property
|title=Learning Real-Time Automata from Multi-Attribute Event Logs
|pdfUrl=https://ceur-ws.org/Vol-655/dynak2010_invited2.pdf
|volume=Vol-655
|dblpUrl=https://dblp.org/rec/conf/pkdd/Kramer10
}}
==Learning Real-Time Automata from Multi-Attribute Event Logs==
<pdf width="1500px">https://ceur-ws.org/Vol-655/dynak2010_invited2.pdf</pdf>
<pre>
           Learning Real-Time Automata from
               Multi-Attribute Event Logs

                                Stefan Kramer

                   Technische Universität München, Germany



Abstract

Network structures often arise as descriptions of complex temporal phenomena in
science and industry. Popular representation formalisms include Petri nets and
(timed) automata. In process mining, the induction of Petri net models from
event logs has been studied extensively. Less attention, however, has been paid
to the induction of (timed) automata outside the field of grammatical inference.
In the talk, I will present work on the induction of timed automata and show how
they can be learned from multi-attribute event logs. I will present the learning
method in some detail and give examples of network inference from synthetic,
medical and biological data.

</pre>