Many information systems nowadays record data about the process instances executed at the organization in the form of traces in a log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining. The approach has been tested in the medical domain of stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.

Towards Semantic Process Mining Through Knowledge-Based Trace Abstraction

Manuel Striani;Silvana Quaglini;Stefania Montani
2017-01-01

Abstract

Many information systems nowadays record data about the process instances executed at the organization in the form of traces in a log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining. The approach has been tested in the medical domain of stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.
2017
Data-Driven Process Discovery and Analysis - 7th IFIP WG 2.6 InternationalSymposium, SIMPDA 2017, Neuchatel, Switzerland, December 6-8, 2017,Revised Selected Papers
Springer
340
45
64
978-3-030-11637-8
https://doi.org/10.1007/978-3-030-11638-5_3
Semantic process mining, Knowledge-based, trace abstraction, Medical applications
Giorgio Leonardi, Manuel Striani, Silvana Quaglini, Anna Cavallini, Stefania Montani
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1687704
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