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.


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

Excellent expertise has been gained thanks to the involvement in several EU projects, as GeoPKDD(www.geopkdd.eu) and MODAP (www.modap.eu). 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.

The main research track of KDDLab in the field of Complex Networks is Multidimensional Network Analysis. Traditionally, Complex Network Analysis has been monodimensional: researchers focused their attention to network with a single kind of relation represented. KDDLab is pushing the research over multidimensional networks, i.e. network with multiple kind of relations, since they are a better model to represent the complexity in reality (transportation, infrastructure and social networks are often multidimensional).

In the era of Big Data the opportunities of discovering knowledge from social big data increase with the risk of privacy and discrimination violation. However, big data analytics and fairness are not necessarily enemies. Sometimes many practical and impactful services based on big data analytics can be designed in such a way that the quality of results can coexist with discrimination and privacy protection. The solution is the application of the privacy-by-design and discrimination-by-design principles.

One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities promise to let us scrutinize the ground truth of individual and collective behaviour at an unprecedented detail and scale. There is an urgent need to harness these opportunities for scientific advancement and for the social good. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open infrastructure, where big data and social mining research can be carried out.

DAPS2017: Data mining for the Analysis of Performance and Success

2017/11/18 09:00 Europe/Rome
2017/11/18 09:00 Europe/Rome
New Orleans, USA
DAPS 2017

The increasing availability of Big Data, able to capture diverse collective phenomena, provides an unprecedented opportunity to explore the patterns underlying success. From the strategies followed by successful sportsmen to the emergence of runaway videos on YouTube, from popularity in social media to rising starts in the scientific enterprise, from widespread technologies to groundbreaking innovations, there is wealth of data that can be explored to answer common questions: How can we measure performance? What are the common patterns of success?

Data analysis & Social Mining for the Interconnected Society @ 3rd EAI International Conference on Smart Objects and Technologies for Social Good

2017/11/29 09:00 Europe/Rome
2017/11/30 18:00 Europe/Rome
Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, Pisa, Italy
Data analysis & Social Mining for the Interconnected Society

The rapid growth of the Internet and the Web, the speed with which global communication and trade now takes place, and the fast spreading around the world of news and information as well as epidemics, trends, financial crises and social: these are all signals that mankind has entered a new era, a new techno-social ecosystem whose inner mechanisms are different from before, and largely unveiled.

Conference on Fairness, Accountability, and Transparency FAT*

2018/02/23 09:00 Europe/Rome
2018/02/24 18:00 Europe/Rome
New York City, USA
FAT* Conference

Algorithmic systems are being adopted in a growing number of contexts. Fueled by big data, these systems filter, sort, score, recommend, personalize, and otherwise shape human experiences of socio-technical systems. Although these systems bring myriad benefits, they also contain inherent risks, such as codifying and entrenching biases; reducing accountability and hindering due process; and increasing the information assymmetry between data producers and data holders.


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