Abstract 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.
Multi-level abstraction for trace comparison and process discovery
MONTANI, STEFANIA;STRIANI, MANUEL;
2017-01-01
Abstract
Abstract 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.