Our Lab

The Knowledge Discovery and Data Mining Laboratory (KDD Lab) is a joint research initiative of ISTI Institute of CNR and the Department of Computer Science of the University of Pisa.

The objective of the research unit is the development of theory, techniques and systems for extracting and delivering useful knowledge out of large masses of data.

Today, knowledge discovery and data mining is both a technology that blends data analysis methods with sophisticated algorithms for processing large data sets, and an active research field that aims at developing new data analysis methods for novel forms of data. On one side, classification, clustering and pattern discovery tools are now part of mature data analysis and Business Intelligence systems and have been successfully applied to problems in various commercial and scientific domains. On the other side, the increasing heterogeneity and complexity of the new forms of data – such as those arriving from medicine, biology, the Web, the Earth observation systems, the mobility data arriving from wireless networks – call for new forms of patterns and models, together with new algorithms to discover such patterns and models efficiently.

In this context, the mission of the KDD laboratory is to pursue fundamental research, strategic applications and higher education in the areas of:

Mobility Data Mining for Science of Cities

Ethical Data Mining

Social Network Analysis and Visual Analytics for Social Mining

Analytical Platforms and Data Infrastructures for Social Mining

Our Skills

It was 1999 when we approached data mining research field. Our exploration of the world of Data is still continuing...

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.

Privacy Risk Assessment

Study and design of methods for assessing privacy risks in data analytics.


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.

Community Analysis

Identify hidden sub-structures within complex networks and exploit them to bound homophilic behaviors.


Mobility Data Analysis

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

Economic Complexity

Understand hidden features of products and customers studying their position in the network built over the market.

Network Dynamics

Track, understand and forecast topological perturbations that affect complex networks as time goes by.


Privacy by Design

Building frameworks to counter the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities.

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.

Spreading, Diffusion and Innovation

Design and develop useful tools for understanding, monitoring and signaling diffusion phenomena.

Well-Being Indicators

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


Discrimination Discovery and Prevention

Design algorithms for discovering discrimination in socially sensitive decision data and for enforcing fairness in data mining models.

Social Network Analysis

Extract meaningful knowledge from complex online and offline social contexts.

Multi-Dimensional Networks

Correlate multiple data sources to build and thus understand semantic enriched descriptions of real world networked contexts.

Big data and social minig
Clip evento BIG DATA IN ACTION Roma
Dino Pedreschi at SASForum 22/04/2015
[HD] Dino Pedreschi -  Towards a Digital Time Machine fueled by Big Data and Social Mining


Fun Facts

Gigabytes of data produced by a single person each year
Millions of Internet users
Millions of Tweets sent per day
Gigabytes of Internet traffic per day


Need info? Want ideas? Write us!

Address @ ISTI

Istituto di Scienza e Tecnologie dell’Informazione
Area della Ricerca CNR
via G. Moruzzi 1
56124 Pisa, Italy

Address @ UniPi

Dipartimento di Informatica
Università di Pisa
Largo B. Pontecorvo 3
56127 Pisa, Italy

Phone Number

Phone: +39 050 621 3013
Fax: +39 050 315 2040



Largo B. Pontecorvo 3
56127 Pisa
via Moruzzi 1
56124 Pisa