Mining declarative models using time intervals

A common problem in process mining is the interpretation of the time stamp of events, e.g., whether it represents the moment of recording, or its occurrence. Often, this interpretation is left implicit. In this paper, we make this interpretation explicit using time intervals: an event occurs somewhere during a time window. The time window may be fine, e.g., a single point in time, or coarse, like a day. As each event is related to an activity within some process, we obtain for each activity a set of intervals in which the activity occurred. Based on these sets of intervals, we define ordering and simultaneousness relations. These relations form the basis of the discovery of a declarative process model describing the behavior in the event log

An SMT-based discovery algorithm for C-nets

Recently, Causal nets have been proposed as a suitable model for process discovery, due to their declarative semantics and the great expressiveness they possess. In this paper we propose an algorithm to discover a causal net from a set of traces. It is based on encoding the problem as a Satisfiability Modulo Theories (SMT) formula, and uses a binary search strategy to optimize the derived model. The method has been implemented in a prototype tool that interacts with an SMT solver. The experimental results obtained witness the capability of the approach to discover complex behavior in limited time.Postprint (published version

Process mining meets abstract interpretation

The discovery of process models out of system traces is an interesting problem that has received significant attention in the last years. In this work, a theory for the derivation of a Petri net from a set of traces is presented. The method is based on the theory of abstract interpretation, which has been applied successfully in other areas. The principal application of the theory presented is Process Mining, an area that tries to incorporate the use of formal models both in the design and use of information systems.Postprint (published version

A genetic approach to architectural pattern discovery

Architectural patterns represent reusable design of software architecture at a high level of abstraction. They can be used to structure new applications and to recover the modular structure of existing systems. Techniques like Architecture Compliance Checking (ACC) focus on testing whether realised artefacts adhere to the architecture. Typically, these techniques require a complete architecture as input. In this paper, we present a genetic approach to express and discover architectural patterns based on the allowed and disallowed dependencies between the pattern elements. Through static ACC, we validate the genuineness of the found instances. Initial validation shows the potential of the approach

EPNML 1.1 : an XML format for Petri nets

This document defines EPNML 1.1, an XML format used for Petri nets in the web application Petriweb and the Petrinet editor Yasper. EPNML 1.1 is an application of PNML, a developing standard XML format for Petri nets: EPNML 1.1 documents are a particular type of PNML documents. EPNML is modified over time to incorporate corrections and extensions. The version described here is EPNML 1.1, first published in October, 2003; this specification has been modified several times since, due to errors found. At the time of writing we are already considering an updated version of the file format itself, which will be called EPNML 1.2; that slightly modified document format is not described here