Length : 15 hours

Aim and Scope

This course offers an introduction to Business Process Mining and outlines some relevant challenges that the research community is today facing.

Process Mining focuses on extracting knowledge from data generated and stored in corporate information systems in order to discover or analyze executed processes. Thanks to process mining algorithms data are analyzed to discover and characterize business processes model. It is then possible to check the conformance of each new process instance with the process model and detect failures or bottlenecks and optimize business processes.

Scheduling

The classes are scheduled in the following way:

• Business Process Intelligence
• Petri Nets: an introduction
• Process Mining
• Process Mining: exercises
• Advanced research goals in Process Mining
• Online Process Mining

Dates & Venue

Lecturer

Dr. Paolo Ceravaolo

Assessor

Dr. Paolo Ceravolo

Length : 20 hours - 4 cfu

Abstract 

Smart cities provide a plethora of data of different kinds that need to be acquired, integrated and processed in order to develop intelligent services for the different actors of the city (municipalities, citizens, students, entrepreneurships, and researchers). Cloud-based solutions that are open-source and exploit open-standards for the management of the city data are considered prominent in order to face the scalability, maintainability and financial issues of small and large cities around the world. In this course we will discuss the infrastructures required for the management and the analysis of smart city data (stored, streams, etc.) of big size and provide insight in the current solutions for the visual development of IoT applications, dashboards, and for extracting knowledge. The experiences that we have gained in participating in several European projects will be presented along with new research directions in the field.

Lecturer

Prof. Marco Mesiti, University of Milan, IT

Dr. Stefano Valtolina, University of Milan, IT

Prof. Payam Barnaghi, University of Surrey, UK

Prof. Paolo Nesi, University of Firenze, IT

Assessor

Prof. Marco Mesiti

Length : 10 hours

Abstract 

The course will focus on the main computational tools and on some processing techniques for multidimensional data.

Dates & Venue

Suggested Readings

Elements of linear algebra. Computer programming.

Lecturer

Prof. Dario Malchiodi

Assessor

Prof. Dario Malchiodi 

Length : 15 hours

Abstract 

Networks are pervasive. Certainly our society heavily relies on computer networks, whose complexity scaled in a few decades from simple links among PCs in the same building to the rich structure of the Internet, in which heterogeneous nodes (ranging from datacenters to smartphones) connect by means of communication channels of diverse nature (cables, fiber optics, radio signals).

Indeed, this is just an example, as networks appear in many others - less obvious - contexts, like the modeling of social relations, the regulatory structure of genes, interactions in molecular dynamics, human transportation and distribution systems, just to name a few.

The PhD course will cover the issues of (a) understanding, extracting and exploiting network structures in various domains (b) modeling networks by means of mathematics (c) solving complex decision problems, and performing advanced analyses, by means of computer science methodologies, that is either selecting suitable existing algorithms and tools, or devising new ones.

The course is composed by two parts, the first focusing on routing, and especially on modeling and analyzing by means of network flows, the second covering the main foundational and algorithmic issues in optimal location and network design.

The 2017/18 year's edition will cover *part 1 (flows)*. Although the topics of the two parts are linked, no background knowledge from either part is strictly necessary to fully understand the lectures of the other.

Suggested Readings

Some working knowledge on mathematical modeling and basic computer programming skills.

Dates & Venue

Lecturer

Prof. Alberto Ceselli

Assessor

Prof. Alberto Ceselli