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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.

Data Science Colloquium 2020

Start:
2020/05/20 02:00 Europe/Rome
End:
2020/06/08 02:00 Europe/Rome
Location:
On Line Virtual Event
Link:
Data Science Colloquium 2020
Description:

20/05/2020
14:00 Statistical physics for the analysis of real-world networks - Tiziano Squartini (IMT)
14:30 Analyses of migration network effects - Jisu Kim (Data Science Ph.D. student XXXIII Cycle)

Cos’è l’intelligenza, non solo artificiale

Start:
2020/06/04 02:00 Europe/Rome
End:
2020/06/04 02:00 Europe/Rome
Speaker:
Dino pedreschi
Location:
Online virtual event
Link:
Cos’è l’intelligenza, non solo artificiale
Description:

Il grande biologo e paleontologo Stephen Jay Gould in Intelligenza e pregiudizio (The Mismeasure of Man, 1981) criticò la pretesa di ridurre l’intelligenza a un’entità da misurare. Cosa significa misurare l’intelligenza? Quale idea si ha di intelligenza quando si pensa di misurarla? Siamo sicuri di essere noi i più intelligenti del pianeta? Che rapporto c’è tra intelligenza individuale e l’intelligenza collettiva, sia nella dimensione umana sia in quella delle macchine?

NetSci2020

Start:
2020/07/06 02:00 Europe/Rome
End:
2020/07/10 02:00 Europe/Rome
Location:
Rome, Italy
Link:
NetSci2020
Description:

NetSci 2020 is the flagship conference of the Network Science Society, which aims to bring together leading researchers and practitioners working in the emerging research area of network science.
The NetSci conference fosters multi-disciplinary communication and collaboration in network science research across computer and information sciences, physics, mathematics, statistics, the life sciences, medicine, food science, neuroscience, environmental sciences, social sciences, finance and business, arts and design.

eXplainable Knowledge Discovery in Data Mining XKDD2020

Start:
2020/09/14 02:00 Europe/Rome
End:
2020/09/14 02:00 Europe/Rome
Location:
Ghent, Belgium
Link:
eXplainable Knowledge Discovery in Data Mining XKDD2020
Description:

In the past decade, machine learning based decision systems have been widely used in a plethora of applications ranging from credit score, insurance risk, and health monitoring, in which accuracy is of the utmost importance. Although the application of these systems may bring myriad benefits, their use might involve some ethical and legal risks, such as codifying biases; jeopardizing transparency and privacy, reducing accountability.

Bias and Fairness in AI

Start:
2020/09/14 02:00 Europe/Rome
End:
2020/09/18 02:00 Europe/Rome
Location:
Ghent, Belgium
Link:
Bias and Fairness in AI
Description:

AI techniques based on big data and algorithmic processing are increasingly used to guide decisions in important societal spheres, including hiring decisions, university admissions, loan granting, and crime prediction. They are applied by search engines, Internet recommendation systems and social media bots, influencing our perceptions of political developments and even of scientific findings. However, there are growing concerns with regard to the epistemic and normative quality of AI evaluations and predictions.

SocInfo2020

Start:
2020/10/06 02:00 Europe/Rome
End:
2020/10/09 02:00 Europe/Rome
Location:
Pisa, Italy
Link:
SocInfo2020
Description:

We are delighted to welcome the 12th International Conference on Social Informatics (SocInfo 2020) in Pisa, Italy, on 6-9 October, 2020.

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