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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.

Research

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

Applied Data Science and Visual Analytics

Data Science aims at discovering patterns and models of human behavior across the various social dimensions, extracting multi-dimensional patterns and models from a vast variety of social data.
Data Science may have a potentially high impact and may generate enormous value to society. It can create new opportunities to understand complex aspects, such as mobility behaviors, economic and financial crises, the spread of epidemics, the diffusion of opinions and so on.

Mobility Data Mining for Science of Cities

The large diffusion of localization technologies and location-based services is leading to the production of large and diversified traces of human mobility, containing the potential to infer models of unprecedented precision and depth. They can be key enablers of many applications, from monitoring urban traffic features to reconstruct inter-city mobility demands and region-scale structures, which could help in making modern urban spaces more sustainable, efficient and comfortable for citizens.

Social Network Analysis and Network Science

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).

Ethical, Trustworthy, Interactive AI

In the era of Big Data person-specific data are increasingly collected, stored and analyzed by modern organizations.
These data typically describe different dimensions of the daily social life and are the heart of a knowledge society, where the understanding of complex social phenomena is sustained by the knowledge extracted from the miners of big data across the various social dimensions by using data mining, machine learning and AI technologies.

Analytical Platforms and Infrastructures for Social Mining

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.

BMDA 2024 - Big Mobility Data Analytics

Start:
2024/03/25 01:00 Europe/Rome
End:
2024/03/25 01:00 Europe/Rome
Location:
Paestum, Italy
Link:
Big Mobility Data Analytics 2024
Description:

From spatial to spatio-temporal and, then, to mobility data. So, what’s next? It is the rise of mobility-aware integrated Big Data analytics. The Big Mobility Data Analytics (BMDA) workshop series, started in 2018 with EDBT Conference, aims at bringing together experts in the field from academia, industry and research labs to discuss the lessons they have learned over the years, to demonstrate what they have achieved so far, and to plan for the future of mobility.

Symposium on Intelligent Data Analysis - IDA2024

Start:
2024/04/24 02:00 Europe/Rome
End:
2024/04/26 02:00 Europe/Rome
Location:
Stockholm, Sweden
Link:
Symposium on Intelligent Data Analysis (IDA 2024)
Description:

Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation. Therefore IDA accepts all inspiring papers for both presentation and publication. In order to create an open atmosphere that encourages discussion, the IDA symposium is intentionally small-scale and single-track.

Publications

Fun Facts


3
Gigabytes of data produced by a single person each year
3100
Millions of Internet users
500
Millions of Tweets sent per day
2300
Gigabytes of Internet traffic per day

Contacts

Need info? Want ideas? Write us!

Address @ ISTI

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

Address @ UniPi

KDD Lab
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

Email

kddlab-info@isti.cnr.it