Ricerc@Sapienza

Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual behavior of these processes. One of the most widely studied process mining operations is automated process discovery. An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log.

The integration of business process management with blockchains across organisational borders provides a means to establish transparency of execution and auditing capabilities. To enable process analytics, though, non-trivial extraction and transformation tasks are necessary on the raw data stored in the ledger. In this paper, we describe our approach to retrieve process data from an Ethereum blockchain ledger and subsequently convert those data into an event log formatted according to the IEEE Extensible Event Stream (XES) standard.

Declarative process models define the behaviour of processes by means of constraints exerted over their activities. Multi-perspective declarative approaches extend the expressiveness of those constraints to include resources, time, and information artefacts. In this paper, we present a fast distributed approach and software prototype to discover multi-perspective declarative models out of event logs, based upon parallel computing.