The Big and Open Data Innovation Laboratory (BODaI-Lab) of the University of Brescia, Italy, aims to create working groups that develop – within specific projects – innovative methods, techniques and tools for the retrieval, management and analysis of open and big data with a multidisciplinary approach.

The main purpose is to support research within the University of Brescia, in the fields of medical, engineering, economic, financial, business, social and legal scientific research. Particular attention is devoted to technology transfer to the PA and the Industry sector.

Description       Research Projects

Meaning of BODaI

Bodai (ぼだい) is the Japanese version of the Sanskrit bodhi (बोधि). It is the state of the completely enlightened mind, i.e. the knowledge or wisdom, or awakened intellect, of a Buddha.

The verbal root budh- means to awaken. This term, although mostly used the context of Buddhism, is also present in other Eastern philosophies and traditions. It has been popularised in the Western world with the word enlightenment, having the connotation of a sudden insight into a transcendental truth or reality

In our Laboratory, wisdom, knowledge of Nature and enlightenment are achieved through data analysis and machine learning algorithms.

The symbol used for the “O” of BODaI (as well as for the “O”s in the BODaI’s projects logos) is the sacred symbol in Zen Buddhism called enso, literally meaning circle. It symbolizes absolute enlightenment, strength, elegance, the beginning and end of all things, the circle of life and the connectedness of existence. In two words, Universe and Nature. At the same time, through the impossibility of creating the perfect circle freehand, it contains the lesson of the limits of the human mind and the acceptance of imperfection as perfect.

Some authors consider enso as a precursor of the mathematical symbol of infinity.



Main topics

BODaI-Lab deals with developing methods, models and advanced techniques of multivariate statistical data analysis, machine learning, artificial intelligence, semantic and social web, useful for research, organization, classification, integration, analysis and visualization of huge, heterogeneous and complex collections of digital data (big data), even in open format (open data).

The research activity of the laboratory is focused on the following themes:

  • Design of advanced methods and tools for information sharing and cooperation within contexts featured by a large information background (big&open data)
  • Definition of advanced models and methods for knowledge representation, ontology-based semantic processing, reasoning under uncertainty and incompleteness conditions, logic inference, planning, automatic reasoning and, in general, efficient processing focused on problem solving.
  • Development of mathematical and statistical models for data analytics, of methods and tools for statistical analysis, data mining, knowledge discovery and machine learning, data analysis and evaluation, advanced simulation and optimisation
  • Definition of a software infrastructure, based on Semantic Web and Social Web technologies, including Linked Data techniques ( and social networking, to classify and cluster big&open data from heterogeneous sources, based on their semantics

BODaI-Lab research projects

Scientific coordinators: Paola Zuccolotto and Marica Manisera

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Scientific coordinators: Eugenio Brentari and Luigi Odello

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Scientific coordinator: Devis Bianchini

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Scientific coordinator: Stefano Calza

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Machine Learning methods for Variable Importance Measurement (VIM) and Variable Selection

The project aims at developing machine learning methods and tools for Variable Importance Measurement (VIM) and Variable Selection in statistical prediction problems. The topic is analyzed from a methodological and empirical point of view; specific computational functions are built for the new proposed procedures. From the point of view of the methods, the main focus is on ensemble learning techniques.

Scientific coordinators: Paola Zuccolotto, Marco Sandri

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Statistics, data mining and machine learning for the analysis of microarray and RNA-Seq gene expression data in grapevine

The project is carried out in collaboration with researchers of the Department of Biotechnology, University of Verona. Microarray and RNA-Seq gene expression data in grapevine are analyzed with statistical methods and data mining/machine learning algorithms in order to detect associations and relationships with reference to genotype, environment, developmental stages and interactions among them.

Scientific coordinators: Paola Zuccolotto, Marco Sandri

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Data Science for Brescia – Arts and Cultural Places

The Project “Data Science for Brescia – Arts and Cultural Places” (DS4BS) has the main objective to increase knowledge about the way people visit the cultural places (museums, theaters, monuments and historic buildings) in the city of Brescia, in order to support institutions and decision makers. Special attention is devoted to the experimentation of new ways for public detection and engagement, exploring cultural attitudes and perceptions, and developing new forms of accessibility to culture, also with reference to the cultural tourism.

Scientific coordinator: Marica Manisera

Participants: Devis Bianchini, Barbara Rita Barricelli, Maurizio Carpita, Daniela Fogli, Paola Zuccolotto

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Analysis of Rating Data in the CUB class framework

This page collects the main methodological advancements developed by the BODaI-Lab research group on the topic of rating data analysis by means of statistical models in the CUB class. The original CUB (Combination of Uniform and Binomial) model was introduced in 2005 by D’Elia and Piccolo. Since then, several extensions have been proposed worldwide. The BODAI-Lab research group gave birth to NLCUB (NonLinear CUB) in 2014 and to CUM (Combination of Uniform and Multinomial) in 2022. In addition, the problem of “don’t know” responses (DK) has been considered from a methodological point of view and a novel proposal has been advanced in this context, able to treat DK as valuable information, instead of missing data as usual.

Scientific coordinators: Domenico Piccolo, Paola Zuccolotto, Marica Manisera, Rosaria Simone

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Machine Learning for Survival Data

The project aims at studying machine learning algorithms for survival analysis, with main focus on survival trees and survival random forests. The topic presents still some unclear points, mainly dealing with performance assessment. Moreover, there is still not an harmonization of all the proposed methods. An analysis from both a theoretical and practical point of view is carried out with the aim of sheding light on the topic.

Scientific coordinators: Ambra Macis, Marica Manisera, Marco Sandri, Paola Zuccolotto.

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Statistical Models for Textual Data Science

Scientific coordinators: Riccardo Ricciardi, Marica Manisera, Paola Zuccolotto, Maurizio Carpita.

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Smart Solutions for Cyber Physical Production Systems

Scientific coordinator: Devis Bianchini

Data mining and machine learning for the development of risk-predicting models in patients with Covid-19 pneumonia, based on the chest x-ray Brixia-severity score and lab tests

The project is part of the actions aimed at contrasting Covid-19 infection through the development of prognostic risk models based on quantitative data related to lung damage and biochemical tests, detected in 1300 patients affected by Covid-19 in course of hospitalization at ASST Spedali Civili Brescia. Lung damage assessment is performed by Brixia-severity radiological score and the state of tissue inflammation through the biochemical data PCR, ferritin, LDH, troponin, D-Dimer, fibrinogen, WBC. The methods are based on multivariate statistical analysis, data mining and artificial intelligence algorithms.

Scientific coordinators: Roberto Maroldi, Alfonso Gerevini, Paola Zuccolotto

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Human Activity Spatio-Temporal Indicators using Mobile Phone Big Data

The project, developed by the DMS StatLab of the University of Brescia in agreement with the Statistical Office of the Municipality of Brescia, is based on the use of high frequency mobile phone data to develop spatio-temporal indicators useful for statistical analyses.

Scientific coordinator: Maurizio Carpita

Participants: Anna Simonetto, Rodolfo Metulini, Marie Cointin

ODE — Open Data Exploration

This project is developed in cooperation with Regione Lombardia, aiming at investigating techniques for exploration and knowledge extraction from Open Data (in particular, focusing on data about mobility) provided by Regione Lombardia on its official web portal (

Scientific coordinator: Devis Bianchini

Banco Energia — Interventions against energy poverty

Project developed within the two-years agreement between University of Brescia, Università Cattolica in Brescia and A2A S.p.A. The project will provide essential information for preparing A2A initiative for vulnerable consumers. In particular, the project includes both support for the identification of potential beneficiars and the implementation of field experiments to identify more effective procedures aiming at stimulating contributions to the project from consumers.

Scientific coordinator: Raffaele Miniaci

Labor contracts, incentives for productivity and innovation: theoretical and empirical analysis

Scientific coordinator: Raffaele Miniaci