Mobility Data Mining for Science of Cities

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Excellent expertise has been gained thanks to the involvement in several EU projects, as GeoPKDD( and MODAP ( A concrete recent achievement is the realization of the system M-ATLAS as an platform to support the mobility knowledge discovery process, from data preprocessing, to data mining to semantic enrichment and patterns interpretation.

Data Mining

Analysis methods and tools to extract knowledge hidden in the data, including frequent patterns, clustering and classification.

Data Visualization

Visual representation coupled with advanced analytics to comprehend and understand complex and large data.

Data Science

A combination of analytic, machine learning, data mining and statistical skills as well as experience with algorithms and technological tools.

Big Data

Acquiring strategies to manage and analyse large data sets and related tools such as MapReduce, Spark, Hive and Pigas well as NoSQL databases.

Mobility Data Analysis

Inferring human mobility information from location data sources such as GPS trajectories, mobile phone traces and social media.

Science of Success

Understanding the patterns of success in several fields: sports performance, popularity of artistic items, emergence of new technologies.

Sports Data Mining

Developing new methods of performance measurement by taking advantage of the huge growth of data collected during sport events.


Design algorithms for estimating the distribution of a population across different classes, and for tracking the changes in this distribution.

Well-Being Indicators

Developing of models to predict the well-being of territories based on Big Data on human behavior.