Abstract
Many information systems nowadays record data about the process instances executed at the organization in the form of traces in an event 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.
Lingua originale | Inglese |
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pagine (da-a) | 98-112 |
Numero di pagine | 15 |
Rivista | CEUR Workshop Proceedings |
Volume | 2016 |
Stato di pubblicazione | Pubblicato - 2017 |
Evento | 7th International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2017 - Neuchatel, Switzerland Durata: 6 dic 2017 → 8 dic 2017 |