Stefano
Pio Zingaro


Pio Zingaro
Adjunct Professor University of Bologna

BSc in Computer Science at the University of Trento, MSc in Bioinformatics, and PhD at the Department of Computer Science and Engineering (DISI), University of Bologna. Member of the Focus Research Team at INRIA and the SPACES research group.  In 2019, He completed his PhD supervised by Professor Maurizio Gabbrielli and Professor Ivan Lanese . The research focused on the exploitation of Microservices-Oriented Computing in the development of interoperable application for Internet of Things systems. Currently, He is involved in a Post-Doctoral position at DISI. The project aims to take advantage of Machine Intelligence techniques and Edge Computing to accomplish proper virtualization and to achieve optimal workload mobility of software components in Industrial Internet of Things (IIoT) settings.

COURSES

Managers and Domain Experts face quite often the challenge of taking decisions on complex matters, characterized by a highly complex scenario, and huge quantity of data and/or knowledge and/or analytical models. Decision Support Systems (DSS) are a class of tools that help the users to understand pros and cons of possible alternative decisions, thus augmenting the awareness of the decision maker about the decision process and its impact.

A first part of the course is devoted to tools and solutions for representing the knowledge and the decision process adopted by Domain Experts. Decision criteria and rules can be documented in a formal, still human-readable way and, more important, can be used to feed software (such as Rule Engines) to fully automatize part of, or whole decision processes. Recent standards like, e.g., the Decision Model and Notation (DMN), will be introduced during a hands-on session, where the audience will directly experiment the advantages of documenting and automatizing decision processes by means of rule-based approaches.

A second part of this course is devoted to introduce the audience to DSS that exploit data and machine learning techniques to learn models of complex domains. These models are in turn used to predict and simulate the scenarios, so as to compare alternative decisions and their expected effects. Visualization techniques then help the decision maker to confront different decisions, w.r.t. optimal solutions for a given objective, or multi-objective decision criteria.

Digital Technology and Innovation Management