Purpose of the Process & Data Intelligence (PDI) research unit is to study and develop technologies that support the formalization, retrieval, and usage of knowledge pertaining (business) processes and the healthcare and wellbeing world. Current research efforts pertain: 

Process Discovery & Deviance Mining

The aim of process discovery is to build a process model from an event log without prior information about the process. We are working towards the discovery of different types of BUsiness Porcess models starting from different types of data sources. Examples are: The discovery of Hybrid Process Models composed of a mixture of informal causal graphs and formal Petri Nets; the discovery of (Declarative) Process Models built from positive and negative execution traces; the discovery of (Procedural) Process Models from text; and  the discovery of (Declaratve) discriminative patterns between positive and negative execution traces.

Process Verification and Conformance Checking

We are intersted in providing theoretically sound, yet practically viable support for the verification of data aware procedural process models.  In particular we have exploited planning techniques to perform reachability analysis, and conformance checking also in the event of partial execution traces. The latter problem has been explored also with the usage of abduction.  We are also interested in exploiting formal verificatin techniques, such as the computation of unsat cores, to support the improvement of Declare models.

Predictive Process Monitoring

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 techniques and support the Nirdizati tool that exploit event logs to predict how ongoing (uncompleted) cases will unfold up to their completion. We work on a wide series of prediction tasks using different types of Machine Learnig techniques, including the insertion of background knowledge. Recent work aims at supporting explainable prediction both in terms of improving the techniques and assess their usefulness with end users.

Explainable Inferences

Modern Artificial Intelligence (AI) has a tremendous potential in supporting humans towards efective decision making in a number of different ways ranging frm prediction, to recommendations, to optimization and so on. The complexity of modelrn AI techniques and the sensitivity of the decision maing task, nonetheless requires these techniques to be robust and thrustworthy. We are interested in investigating explainable AI techniques to suport thrustworthy predictive process monitoring, ….

Opinion Mining and Sentiment Analysis

This research effort aims at (i) detecting the presence of opinions within a resource and (ii) classifying this resource according to the opinion they express about a given subject. Generally speaking, opinion mining and sentiment analysis aim at determining the attitude of a speaker or a writer with respect to a topic or the overall tonality of a document. In the recent years, the exponential increase in the use of the Web for exchanging public opinions about events, facts, products, etc. led to an extensive usage of opinion mining and sentiment analysis approaches, especially for marketing and social purposes.

Knowledge-based Information Retrieval

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