The goal of process mining is to extract process-related information by observing events recorded in event logs. An event is an activity initiated or completed by a resource at a certain time point. Organizational mining is a subfield of process mining that focuses on the organizational perspective of a business process. It considers the resource attribute and derives a profile that characterizes the behavior of a resource in a specific business process. By relating resources associated with correlated profiles, it is possible to define a social network. This paper focuses on the idea of performing organizational mining of event logs via social network mining. It presents a framework that resorts to a stream representation of an event log. It adapts the time-based window model to process this stream, so that window-based social resource networks can be constructed, in order to represent interactions between resources operating at the data window level. Finally, it integrates specific algorithms, in order to discover (overlapping) communities of resources and track the evolution of these communities over consecutive windows. This paper applies the defined framework to two real event logs.

Discovering and tracking organizational structures in event logs

APPICE, ANNALISA;MALERBA, Donato
2016-01-01

Abstract

The goal of process mining is to extract process-related information by observing events recorded in event logs. An event is an activity initiated or completed by a resource at a certain time point. Organizational mining is a subfield of process mining that focuses on the organizational perspective of a business process. It considers the resource attribute and derives a profile that characterizes the behavior of a resource in a specific business process. By relating resources associated with correlated profiles, it is possible to define a social network. This paper focuses on the idea of performing organizational mining of event logs via social network mining. It presents a framework that resorts to a stream representation of an event log. It adapts the time-based window model to process this stream, so that window-based social resource networks can be constructed, in order to represent interactions between resources operating at the data window level. Finally, it integrates specific algorithms, in order to discover (overlapping) communities of resources and track the evolution of these communities over consecutive windows. This paper applies the defined framework to two real event logs.
2016
9783319393148
9783319393148
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/173705
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 5
social impact