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Research

The efficiency of complex organizations depends, in a fundamental way, by their ability to support complex processes. A key step towards this goal is the explicit representation of key aspects of an organization in the form of conceptual models and their alignemnt with data that represent what actually happens in that scenario or organization. This, in order to describe what happens (or should happen), to monitor and analyse what's happening, to predict what will happen, to improve the way things are done, and so on. Purpose of the Process & Data Intelligence (PDI) research unit is to study and develop technologies that support the discovery, modeling, monitoring, and validation of complex organizational models and data. In particular PDI focuses on two areas of research:

  1. Process & Data Mining: Development of technologies and software tools to discover, analyze, predict and validate business processes and data mainly from event logs; 
  2. Knowledge Discovery & Retrieval: Development of technologies and software tools for the automatic discovery of knowledge from data and content, with particular emphasis on matching knowledge (ontology and process matvhing), semantic sentiment analysis / opinion mining, and semantic information retrieval.

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.

The growing adoption of IT-systems for modeling and executing (business) processes or services has thrust the investigation towards techniques and tools which support complex forms of process analysis. Many of them, such as conformance checking, process alignment, mining and enhancement, rely on complete observation of past (tracked and logged) executions. In many real cases, however, the lack of human or IT-support on all the steps of process execution, as well as information hiding and abstraction of model and data, result in incomplete log information of both data and activities. This research activity tackles the issue of automatically completing traces with missing information by notably considering not only activities but also data manipulated by them. Our techniques rely on authomated reasoning techniues such as abduction or planning to return solutions.

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.

Opinion Mining and Sentiment Analysis are natural language processing tasks which 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.

Given a document collection, Information Retrieval is the task of returning the most relevant documents for a specified user query.
Approaches in this field aim to improve information retrieval performances by exploiting knowledge extraction techniques.
Query and documents are expanded with semantic terms obtained by processing them with Natural Language Processing methods (e.g., Entity Linking, Frame Detection), and by linking them to available Semantic Web and Linked Open Data knowledge bases (e.g., DBpedia, YAGO).