TY - JOUR
T1 - Multi-level abstraction for trace comparison and process discovery
AU - MONTANI, Stefania
AU - LEONARDI, GIORGIO
AU - STRIANI, Manuel
AU - Quaglini, S.
AU - Cavallini, A.
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017
Y1 - 2017
N2 - Many information systems record executed process instances in the event log, a very rich source of information for several process management tasks, like process mining and trace comparison. In this paper, we present a framework, able to convert activities in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Our abstraction mechanism manages non trivial situations, such as interleaved activities or delays between two activities that abstract to the same concept. Abstracted traces can then be provided as an input to an intelligent system, meant to implement a variety of process management tasks, significantly enhancing the quality and the usefulness of its output. In particular, in the paper we demonstrate how trace abstraction can impact on the quality of process discovery, showing that it is possible to obtain more readable and understandable process models. We also prove, through our experimental results, the impact of our approach on the capability of trace comparison and clustering (realized by means of a metric able to take into account abstraction phase penalties) to highlight (in)correct behaviors, abstracting from details.
AB - Many information systems record executed process instances in the event log, a very rich source of information for several process management tasks, like process mining and trace comparison. In this paper, we present a framework, able to convert activities in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Our abstraction mechanism manages non trivial situations, such as interleaved activities or delays between two activities that abstract to the same concept. Abstracted traces can then be provided as an input to an intelligent system, meant to implement a variety of process management tasks, significantly enhancing the quality and the usefulness of its output. In particular, in the paper we demonstrate how trace abstraction can impact on the quality of process discovery, showing that it is possible to obtain more readable and understandable process models. We also prove, through our experimental results, the impact of our approach on the capability of trace comparison and clustering (realized by means of a metric able to take into account abstraction phase penalties) to highlight (in)correct behaviors, abstracting from details.
UR - https://iris.uniupo.it/handle/11579/82020
U2 - 10.1016/j.eswa.2017.03.063
DO - 10.1016/j.eswa.2017.03.063
M3 - Article
SN - 0957-4174
VL - 81
SP - 398
EP - 409
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -