The aim of process discovery is to build a process model from an event log without prior information about the process. The discovery of declarative process models is useful when a process works in an unpredictable and unstable environment since several allowed paths can be represented as a compact set of rules. One of the tools available in the literature for discovering declarative models from logs is the Declare Miner, a plug-in of the process mining tool ProM. Using this plug-in, the discovered models are represented using Declare, a declarative process modelling language based on LTL for finite traces. In our work, we use a combination of an Apriori algorithm and a group of algorithms for Sequence Analysis to improve the performances of the Declare Miner.
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Process mining focuses on the development of process management techniques that allows for the analysis of business processes based on event logs. In our group we focus on a number of techniques for the discovery of declarative processes and for the predictive monitoring of processes.
Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this reserach line we investigate and produce tools that exploit event logs to predict how ongoing (uncompleted) cases will unfold up to their completion. We actively work towards the development of a predictive process monitoring framework that collects a range of techniques that allow users to get different forms of accurate predictions. For instance on the achievement of a goal or the time required for such an achievement.
While BPM is a mature field for what concerns modeling and enactment of business processes, it is still lacking in the proper support and analysis of the active process executions. The main goal of the KAOS project is to overcome such issues by empowering OS with domain knowledge. In particular, KAOS will develop a foundational framework of concepts covering organizations, processes, participants, and information as relevant for Knowledge-empowered OS. It will then exploit this framework as the basis for the development of a new generation of OS techniques truly flexible and able to support domain experts and business analysts in the effective execution of business processes.
ThePreMo tool is a predictive process monitoring framework for estimating the likelyhood that an ongoing case will lead to a certain outcome among a set of possible outcomes. It includes a range of techniques to adapto to different scenarios as well as hyperparameter optimization methods to select a suitable framework instance for a given dataset.