	AULA MAGNA	AULA ALFA	AULA LAB MAGISTRALE (3rd FLOOR)
8:30-17:30	Registration
8:45-9:00	Welcome
9:00-11:00	TOREADOR Heritage	Workshop on Methods and Applications of Process Science - MAPS 2022
11:00-11:15	Coffee Break
11:15-13:00	SoBigData.eu: A Research Infrastructure to Empower Data Science Analysis	Workshop on Methods and Applications of Process Science - MAPS 2022
13:00-14:00	Lunch
14:00-15:00	User-Centric Big Data	Big Data Efficiency and Reliability	Workshop on Data Science Education, current state and future challenge
15:00-16:00	Workshop on Active Projects of the CINI Lab on Big Data	Information Extraction from Big Data	Workshop on Data Science Education, current state and future challenge
16:00-16:15	Coffee Break
16:15-17:15	Workshop on Active Projects of the CINI Lab on Big Data	Workshop on Data Governance and Analysis in the Domain of Justice	Workshop on Data Science Education, current state and future challenge
17:15-18:00	Panel on "Big Data for Data Science"	Workshop on Data Governance and Analysis in the Domain of Justice	Workshop on Data Science Education, current state and future challenge
18:00-19:30	CONGRESS RECEPTION

	AULA MAGNA	AULA ALFA
8:30-18:00	Registration
8:45-9:00	Welcome
9:00-9:45	Keynote 1 - Stefano Ceri: Storing and Analyzing Viral Sequences Through Data-Driven Genomic Computing
9:45-10:45	Big Data and AI I	Home EnergIA - NILM
10:45-11:00	Coffee Break
11:00-12:00	Big Data and AI II	Studying Fake News Spreading, Polarization Dynamics, and Social Media Manipulation
12:00-12:45	Awards and PhD Awardee Speech
12:45-13:45	Lunch
13:45- 14:30	Keynote 2 - Francesco Bonfiglio: Gaia-X: federating dataspaces via an open-source architecture framework
14:30-15:15	Panel on Data Sovereignty and Data Governance: The Role of Open Science and Open Data
15:15-15:30	Coffee Break
15:30-16:00	Big Data Lakes and Databases	Big Data Processing and Integration
16:00-16:30	Domain-Specific Big Data
16:30-17:30	Novel Applications of Big Data	Workshop on Intelligenza Artificiale, Algoritmi e Discriminazione Sociale
17:30-18:00	What is the shape of a neural circuit? Data, featured network and neurons.	Workshop on Intelligenza Artificiale, Algoritmi e Discriminazione Sociale
18:00-18:15	Closing and ITADATA2023 (plenary)

Workshop on Methods and Applications of Process Science - MAPS 2022

Abstract of the workshop:

Process Science is an emerging multidisciplinary approach to support business process management using concepts and tools of computer science, management science, and information systems. Process Science aims at gathering a better understanding of application, organizations, or systems by studying their events in terms of coherent series of changes that evolves over time, occur at various levels, and perform functions. The ubiquitous availability of data, combined with advanced data analytics capabilities and artificial intelligence methods offers new opportunities to study processes integrating quantitative and qualitative approaches in unprecedented automated procedures.

Workshop goals, objectives, outcomes:

The workshop aims at fostering the interaction between members of the national and international research community working on the different aspects of process science. This includes industrial and academic researchers and other professionals who are actively working in the field. Participants are welcome to present their ongoing initiatives and experience either as talks or demonstrations. Commercial products presentation will not be considered for presentation.
Topics of interest (include but are not limited to):

Process profiling and variant analysis
Performance mining
Trace clustering
Comparative process mining
Process discovery
Conformance checking
Process optimization
Real-time process monitoring
Predictive process monitoring
Root cause analysis
What-if analysis with process data
Decision mining
Decision point analysis
Process data augmentation
Process simulation

Organizers

Chair: Paolo Ceravolo, Università degli Studi di Milano, Italy, ORCID
Program Committee:

Annalisa Appice, annalisa.appice@uniba.it
Pasquale Ardimento, pasquale.ardimento@uniba.it
Lerina Aversano, aversano@unisannio.it
Sylvio Barbon, sylvio.barbonjunior@units.it
Valerio Bellandi, valerio.bellandi@unimi.it
Marta Cimitile, marta.cimitile@unitelmasapienza.it
Ernesto Damiani, ernesto.damiani@unimi.it
Maggi Fabrizio, maggi@inf.unibz.it
Mario Luca Bernardi, bernardi@unisannio.it
Donato Malerba, donato.malerba@uniba.it
Gabriel Marques, gabriel.tavares@unimi.it

Note

Submissions can be in different formats including, abstracts, slides, demos, and short papers. In general, for each proposal, the authors must submit 2 pages describing the presentation content. Presentations can include live demonstrations of tools and techniques. Also, walkthroughs of attack scenarios are welcome.
The program committee will be in charge of the review of the proposed presentation and of the related selection process. The committee will consider, in particular, the potential interest of the expected audience for the presentations.
There will be no official publication for the presented works, but we will make available on the website the material that the authors want to share with the workshop community.
Submissions can be sent to paolo.ceravolo [at] unimi.it by July 31st.

Program

9:00 - 9:10 - Opening
9:10 - 9:30 - Data-aware next activity explainable prediction - Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Chiara Verdone
9:30 - 9:50 - Meta-learning for automatic Process Mining - Sylvio Barbon Junior, Paolo Ceravolo, Gabriel Marques Tavares
9:50 - 10:10 - Data-Aware Process Discovery for Malware Detection: An Empirical Study - Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Chiara Verdone, Fabrizio Maggi
10:10 - 10:30 - Log2Enc: an event log encoding comparison tool - Gionata Forello, Gabriel Marques Tavares, Paolo Ceravolo
10:30 - 10:50 - Rule Mining with RuM Francesco Riva, Fabrizio Maria Maggi
Coffee Break
11:15 - 11:35 - Leveraging predictive process mining in process discovery - Vincenzo Pasquadibisceglie, Annalisa Appice, Giovanna Castellano, Wil M. P. van der Aalst
11-35 - 11:55 - Challenges and opportunities for applying process mining in understanding coding behavior - Pasquale Ardimento, Mario Luca Bernardi, Marta Cimitile

Workshop on Active Projects of the CINI Lab on Big Data

The workshop aims to provide an overview of the active projects seeing the participation of the CINI Lab on Big Data. Three main projects will be discussed in the workshop as follows.

EU H2020 Project Privacy-First Situational Awareness Platform for Violent Terrorism and Crime Prediction, Counter Radicalisation and Citizen Protection (COUNTER)

aims to develop a frontline community policing tool to counteract radicalisation in Europe. It will draw data from disperse sources into an analysis and early alert platform for data mining and prediction of critical areas such as communities. It will make use of the latest natural language processing technologies. The findings will help practitioners address propaganda with effective counternarratives. The CounteR solution will support information sharing between law enforcement agencies and collaboration between agencies by providing an open platform.

Role of the Lab:

The role of the CINI Lab on Big Data is to develop data collection tools from social media and dark/deep web, on one side, and new analytics algorithms based on machine learning for radicalisation detection.

Presenters:
- Marco Anisetti (Università degli Studi di Milano)
- Michelangelo Ceci (Università degli Studi di Bari)
EU H2020 Project Adaptive multi-tier intelligent data manager for Exascale (ADMIRE)

aims to develop an adaptive storage system that should allow high-performance computing systems to deliver very high throughput and increase application performance. The aim is to significantly improve the runtime of applications in fields such as weather forecasting, remote sensing and deep learning.

Role of the Lab:

The CINI unit of the project sees the collaboration between the CINI Lab on High PerformanceComputing (leading the unit) and the CINI Lab on Big Data (participating to the unit). The role of the CINI Lab on Big Data is to define and develop the proper data analytics techniques for increasing the effectiveness and performance of HPC architectures

Presenters:
- Marco Aldinucci (Università di Torino)
- Massimo Torquati (Università di Pisa)
EU H2020 Project Intelligent Management of Processes, Ethics and Technology for Urban Safety (IMPETUS)

aims to develop an instrument that covers the entire physical and cybersecurity value chain, increasing city resilience to security events in public areas. It will apply IoT, AI and Big Data technologies, ensure smart city capabilities in protecting personal data and establish a multi-tenant solution entirely coordinated with the operational needs of a wide range of city stakeholders.

Role of the Lab:

The role of the CINI Lab on Big Data is to define and deploy a big data engine with a novel data governance solution based on access control, on one side, and to implement new machine learning algorithms for anomaly detection and event recognition.

Presenters:
- Claudio A. Ardagna (Università degli Studi di Milano)
- Chiara Braghin (Università degli Studi di Milano)
- Michelangelo Ceci (Università degli Studi di Bari)

Workshop on Data Science Education, current state and future challenge

Organizers and Chairs: Andrea Maurino (UNIMIB) and Silvia Salini (UNIMI)

14.00 – 14.15 – Andrea Maurino and Silvia Salini - Welcome greetings

14.15 – 15.00 Data Scientist skills and knowledge

Mauro Gasparini - POLITO - "Just what probability and statistics should a Data Science graduate know?"
Letizia Tanca – POLIMI - "Data preparation for a quality-aware Data Science" ?
Paolo Giudici (with Emanuela Raffinetti) - UNIPV – “Explainable artificial intelligence in finance and health care: the experience of the University of Pavia”

15.00 – 16.00 Bachelor Programs

Maria Lucia Parrella (with Marcella Niglio and Michele La Rocca) - UNISA - Statistics for Big Data @UNISA: an undergraduate Data Science course of study
Rosanna Verde - UNICAMPANIA - Considerations on the Data Science training on 5 years (3 + 2). The experience of the three-year degree in Data Analytics of the University of Campania.
Marcello Chiodi (with Antonella Plaia and Giada Adelfio) - UNIPA- The multi-year experience of Palermo: from the four-year degree to the bachelor's degrees and master's degrees in statistics and data science

16.00 – 16.15 Coffee Break

16.15 – 17.00 Master Degree Programs

Alfio Ferrara – UNIMI - Perspectives on teaching data science: the experience of the Master Degree in Data Science for Economics of the University of Milan
Andrea Maurino – UNIMIB - Data science master degree at university of Milano Bicocca, the story so far
Roberta Siciliano (with Giuseppe Longo) - UNINA – Experiential Learning in Statistics, Data Science and Artificial Intelligence at the Master Degree in Data Science of University of Naples Federico II

17.00 – 18.00 Future perspectives and Conclusions

What about a Phd in Data Science? (Marcello Chiodi and Paolo Giudici)
What companies want, where and how to find it in candidates (Aldo Razzino CEO Open Data Playground – Hunting Data Professionals)
SIS’s Data Science Group (Nicola Torelli and Rosanna Verde)
CINI’s Laboratory of Big Data (Emanuela Merelli)

Workshop on Intelligenza Artificiale, Algoritmi e Discriminazione Sociale

La consapevolezza che le scelte operate da procedure algoritmiche, anche quando realizzate da processi di apprendimento automatico, non siano intrinsecamente neutrali, è cresciuta in modo significativo negli ultimi anni. Le distinzioni e le discriminazioni sedimentate nella struttura sociale alimentano classificazioni e selezioni errate o forvianti. Diventa quindi necessario sviluppare strumenti di valutazione critica e certificazione della correttezza delle procedure algoritmiche, anche in ottica regolatoria. L'obiettivo del workshop è raccogliere alcune delle più attuali ricerche prodotte a livello nazionale sulla relazione tra algoritmi, intelligenza artificiale e discriminazione sociale, cercando di mettere in collegamento tra loro aspetti aspetti algoritmici e metodologici, normativi e sociopolitici. Inoltre sono di interesse per il workshop casi reali applicati a contesti specifici o generici.

Organizzatori

Valerio Bellandi, Università degli Studi di Milano
Paolo Ceravolo, Università degli Studi di Milano
Marilisa D'Amico, Università degli Studi di Milano

Formato del workshop

Presentazione e selezione

Gli interventi inclusi nel programma potranno essere sia ad invito che sulla base di una proposta al comitato organizzatore. Il comitato prenderà in considerazione, in particolare, il potenziale interesse del pubblico previsto per le presentazioni. Non ci sarà una pubblicazione ufficiale per i lavori presentati, ma renderemo disponibile sul sito web il materiale che gli autori vorranno condividere con la comunità del workshop.

Programma

16:30 - 16:35 - Paolo Ceravolo - Apertura dell'incontro
16:35 - 16:50 - Nannerel Fiano - La legge tedesca sul linguaggio d’odio online: tra tutela della libertà di opinione e tutela della dignità dei singoli
16:50 - 17:15 - Costanza Nardocci - La “nuova” discriminazione artificiale: caratteri e proposte di regolamentazione
17:15 - 17:30 - Camilla Quaresmini - Dimensioni di qualità dei dati per un'IA equa
17:30 - 17:45 - Sara Cinquini - Linguaggio discriminatorio nei testi: un caso pratico nei documenti ufficiali delle P.A.
17:45 - 18:00 - Fabio Massimo Zanzotto - Syntax and prejudice: ethically-charged biases of a syntax-based hate speech recognizer unveiled

Panel on Data Sovereignty and Data Governance: The Role of Open Science and Open Data

The ICT technologies observed several revolutions that tremendously changed the society landscape several times in the last decades. Different technologies have been presented and deployed: the cloud computing paradigm comes to maturity and IoT systems are finally production-ready. Moreover, edge computing is gaining popularity, complementing the cloud by migrating the computation at the edge of the network, closer to end devices. On top of this, 5G networks is providing enhanced connectivity and network with high throughput and low latency, extending smart device support towards massive IoT. This convergence between cloud-edge continuum, IoT, and 5G is exponentially increasing the amount of collected data, which is predicted to reach the order of GeopBytes (10 30 Bytes), pushing towards the development of smart applications running partly at the periphery and partly at the core of the network, embracing more and more domains (homes, transportation, medicals, smart cities and industries, to name but a few).

Today, data science is a key driver in the implementation and working of the above modern systems and smart applications, providing new approaches for fast and accurate data collection, preparation, and analysis. On the other side, the availability of a huge amount of data introduces the need for enhanced data governance and data sovereignty techniques addressing new and critical requirements. This scenario is further complicated by the new paradigms of open science and open data that require to revisit existing data sovereignty and governance techniques and regulations.

This panel will explore the evolving landscape of data sovereignty and governance, and discuss how they can be advanced or affected by the raise of open science and open data.

Panel Chair

Claudio Agostino Ardagna is Full Professor with the Università degli Studi di Milano, the Director of the CINI National Lab on Big Data, and co-founder of Moon Cloud srl. His research interests are in the areas of cloud-edge security and assurance, and data science, where he published more than 140 contributions in international journals, conference/workshop proceedings, and chapters in international books. He is the winner of the ERCIM (European Research Consortium for Informatics and Mathematics) WG STM 2009 Award for the Best Ph.D. Thesis on Security and Trust Management. He has been visiting researcher at Beijing University of Posts and Telecommunications, Khalifa University, George Mason University. He is member of the Steering Committee of IEEE Transactions on Cloud Computing, member of the editorial board of the IEEE Transactions on Cloud Computing and IEEE Transactions on Services Computing, and secretary of the IEEE Technical Committee on Services Computing. He is Program Chair in Chief for IEEE SERVICES 2023.

Panelists

Maria Pia Abbracchio is Full professor of pharmacology at the University of Milan, where she also currently serves as Vice-Chancellor and Vice Rector for Research and Innovation. She is also Vice President of Fondazione Unimi that aims at translating results from basic research into products and applications for human, animal and environmental health. Her research is mainly focussed on the set up of novel therapies for neurodegenerative diseases, with an increasing interest in Big Data. She is author/coathor of more that 200 scientific publications in international indexed Journals and she has been granted various Scientific Prizes. In 2014 she has been nominated Commander of the Highest Order of Merit of the Italian Republic (motu proprio by President Giorgio Napolitano) and she is President Elect of Gruppo2003 per la ricerca scientifica, that gathers the most cited Italian scientists (“highly cited scientists”, less than 0.5% of all publishing scientists). She is also member elect of the Lombardy's Life Sciences Cluster that brings together public and private players in diagnostics, advanced therapies, pharmaceuticals, medical devices and healthcare technologies, to create new business opportunities for its territory.

Francesco Bonfiglio joined GAIA-X in March 2021 as the new CEO.
With more than 30 years' experience in the business of consulting and information technology, Francesco brings his knowledge of the cloud and data market needs, and his vision on how Gaia-X should revolutionize it. Independent Advisor since 2020, he has spent his previous professional life as an executive in many Italian and Multinational contexts. Formerly Chief Executive Officer for ENGINEERING D.HUB (Hybrid Cloud and Digital Transformation company within the largest Italian System Integrator Group), Vice President for Technology Services at AVANADE and Managing Director within the ACCENTURE Group, Chief Technology Officer for HEWLETT-PACKARD EMEA, SW Factory Director at UNISYS, Technical Director and Methodology Evangelist at RATIONAL SOFTWARE amongst his positions.
With a background in Electronics Engineering, he started his career in the late 80s as a HW/SW Engineer working on some of the most advanced Military R&D projects, from the Sonar System for the Italian Navy (ANSQQ14IT) prized during the Gulf war for the mine-hunting performances, to the Eurofighter Fighter Aircraft (EFA), still one of the most advanced examples of technology in the Military field.
Francesco had a bottom-up career, covering all roles within the IT, from developer to top-executive, from R&D to business units' leader. An early supporter of the need for continuous innovation, he is a big fan of the startup ecosystem, active member of evaluation commissions, and being board member and co-founder of FOOLFARM.com, the first example of Startup Studio in Italy.
Amongst other roles Francesco has represented the IT world and then became Vice President of Confindustria (Italian Trade Association) for Valle D'Aosta.
Living in Italy, in the countryside out of Milan, married and father of three sons, he loves music and played in several independent live and record productions since the 80s.
Francesco believes in the power of collective intelligence, lateral thinking, and teamwork, as a propeller for transformation, in the business as well as in the whole life!

Angela Bonifati (PhD, 2002) is a Professor of Computer Science at Lyon 1 University and at the CNRS Liris research lab, where she leads the Database Group. She is also an Adjunct Professor at the University of Waterloo in Canada since 2020. Her current research interests are on the interplay between relational and graph-oriented data paradigms, particularly query processing, indexing, data integration and learning for both paradigms. She is involved in several grants at Lyon 1 University, including French, EU H2020 and industrial grants. She has also co-authored more than 150 publications in top venues of the data management field, and is the recipient of two Best Paper awards (ICDE22, VLDB22 runner up). She has co-authored two books (on Schema Matching and Mapping edited by Springer in 2011 and on Querying Graphs edited by Morgan & Claypool in 2018) and an invited paper in ACM Sigmod Record 2018 on Graph Queries. She was the Program Chair of ACM Sigmod 2022 and she is currently an Associate Editor for both Proceedings of VLDB and IEEE ICDE. She is an Associate Editor for several journals, including the VLDB Journal and ACM TODS. She is currently the President of the EDBT Executive Board and a member of the Sigmod Executive Committee.

Davide Dalle Carbonare is Senior Researcher and Business Developer for the R&D Department at Engineering Ingegneria Informatica SpA. Since 2005 he is contributing to national and international EU-funded projects focusing on various aspects (big data & AI, open source, service/data platforms, project-automation, team-collaboration) and during the last years he is mostly dedicated to consortium building and project proposal preparation. Since 2014 he is in the Board of Directors of the Big Data Value Association where he also co-leads the working group on Smart Manufacturing Industry and contributes to the forking group on Data Spaces. In addition to that he is member of the FIWARE Technical Steering Committee.

Ernesto Damiani is Full Professor with the Università degli Studi di Milano, Italy, the Senior Director of the Robotics and Intelligent Systems Institute, and the Director of the Center for Cyber Physical Systems (C2PS), Khalifa University, United Arab Emirates. He is also the Leader of the Big Data Area, Etisalat British Telecom Innovation Center (EBTIC) and the President of the Consortium of Italian Computer Science Universities (CINI). He is also part of the ENISA Ad-Hoc Working Group on Artificial Intelligence Cybersecurity. He has pioneered model-driven data analytics. He has authored more than 650 Scopus-indexed publications and several patents. His research interests include cyber-physical systems, big data analytics, edge/cloud security and performance, artificial intelligence, and machine learning. He was a recipient of the Research and Innovation Award from the IEEE Technical Committee on Homeland Security, the Stephen Yau Award from the Service Society, the Outstanding Contributions Award from IFIP TC2, the Chester-Sall Award from IEEE IES, the IEEE TCHS Research and Innovation Award, and a Doctorate Honoris Causa from INSA-Lyon, France, for his contribution to big data teaching and research.

User-Centric Big Data (Research papers)

Chair:

Donato Malerba, Università degli Studi di Bari

Program

Adversarial Attacks in Machine Learning Based Access Control
- Mohammad Nur Nobi (University of Texas at San Antonio)
- Ram Krishnan (University of Texas at San Antonio)
- Ravi Sandhu (University of Texas at San Antonio)

Safe egress scenario detection: from baseline to active events
- Matteo Bottin (University of Padova)
- Paolo Mocellin (University of Padova)
- Chiara Vianello (University of Padova)
- Giulio Rosati (University of Padova)
- Giuseppe Maschio (University of Padova)

In-browser Visualization of Large-scale Graphs
- Luca Consalvi (Università degli Studi di Perugia)
- Walter Didimo (Università degli Studi di Perugia)
- Giuseppe Liotta (Università degli Studi di Perugia)
- Fabrizio Montecchiani (Università degli Studi di Perugia)

Big Data and AI I (Research Papers)

Chair:

Giovanni Stilo, Università degli Studi dell'Aquila, Italy

Program

Anomaly Detection for Physical Threat Intelligence
- Paolo Mignone (University of Bari Aldo Moro)
- Donato Malerba (University of Bari Aldo Moro)
- Michelangelo Ceci (University of Bari Aldo Moro)

A user-centered approach to create realistic datasets for AI. Case study: creditworthiness in the banking sector
- Francesca Zampino (University of Salento)
- Antonella Longo (University of Salento)
- Marco Zappatore (University of Salento)

A Semantic Rule Based Expert System for the Discovery and Composition of Cloud Services
- Beniamino Di Martino (Università della Campania Luigi Vanvitelli)
- Antonio Esposito (Università della Campania Luigi Vanvitelli)
- Michele Di Giovanni (Università della Campania Luigi Vanvitelli)

Big Data and AI II (Research Papers)

Chair:

Marco Ferretti, Università degli Studi di Pavia, Italy

Program

Data Analytics on Twitter for Evaluating Women Inclusion and Safety in Modern Society
- Loredana Caruccio (University of Salerno)
- Stefano Cirillo (University of Salerno)
- Vincenzo Deufemia (University of Salerno)
- Giuseppe Polese (University of Salerno)
- Roberto Stanzione (University of Salerno)

Review Spam Detection using Multi-View Deep Learning Combining Content and Behavioral Features
- Giuseppina Andresini (University of Bari Aldo Moro)
- Andrea Iovine (University of Bari Aldo Moro)
- Roberto Gasbarro (University of Bari Aldo Moro)
- Marco Lomolino (University of Bari Aldo Moro)
- Marco de Gemmis (University of Bari Aldo Moro)
- Annalisa Appice (University of Bari Aldo Moro)

Benchmarking FedAvg and FedCurv for Image Classification Tasks
- Bruno Casella (University of Turin)
- Roberto Esposito (University of Turin)
- Carlo Cavazzoni (Leonardo S.p.A.)
- Marco Aldinucci (University of Turin)

Big Data Lakes and Databases (Research Papers)

Chair:

Marco Ferretti, Università degli Studi di Pavia, Italy

Program

Conceptual Constraints for Data Quality in Data Lakes
- Paolo Ciaccia (University of Bologna)
- Davide Martinenghi (Polytechnic University of Milan)
- Riccardo Torlone (University of Roma Tre)

A Manifesto for Applied Data Science - Reasoning from a Business Perspective
- Bahne Christiansen (Nordakademie University of Applied Sciences)
- Uwe Neuhaus (Nordakademie University of Applied Sciences)
- Michael Schulz (Nordakademie University of Applied Sciences)
- Adrian Hargreaves (Whitireia Polytechnic)
- Antinisca Di Marco (University of L'Aquila)
- Guido Proietti (University of L'Aquila)
- Fabrizio Rossi (University of L'Aquila)
- Giovanni Stilo (University of L'Aquila)
- Mark H. Haney (Chatham University)
- Andrew Duncan (University of the Highlands and Islands Inverness College)
- Daniele Tessera (Università Cattolica del Sacro Cuore)
- Tilman Todt (Hogeschool van Arnhem en Nijmegen)
- Andreas Brandenberg (Lucerne University of Applied Sciences and Arts)
- Patricia Feubli (Lucerne University of Applied Sciences and Arts)

Benchmarking AyraDB next-generation database on Davinci-1 super-computer
- Chiara Francalanci (Polytechnic University of Milan)
- Carlo Cavazzoni (Leonardo S.p.A.)
- Paolo Giacomazzi (Polytechnic University of Milan)
- Niccolò Mangini (Leonardo S.p.A.)
- Roberto Morelli (Leonardo S.p.A.)
- Roberto Ravanelli (Cherrydata srl)

Novel Applications of Big Data (Research Papers)

Chair:

Marco Anisetti, Università degli Studi di Milano

Program

DIORAMA: Digital twIn fOR sustAinable territorial Management
- Giordano d'Aloisio (University of L'Aquila)
- Antinisca Di Marco (University of L'Aquila)
- Giovanni Stilo (University of L'Aquila)
- Andrea Bianchi (University of L'Aquila)
- Andrea D'Angelo (University of L'Aquila)
- Alessandro Di Matteo (University of L'Aquila)
- Jessica Leone (University of L'Aquila)
- Giulia Scoccia (University of L'Aquila)
- Luca Traini (University of L'Aquila)

Analysis of video lessons: a case for smart indexing and topic extraction
- Marco Arazzi (University of Pavia)
- Marco Ferretti (University of Pavia, Department CE)
- Antonino Nocera (University of Pavia)

Web3 social platforms: modeling, mining and evolution
- Cheick Tidiane Ba (Università degli Studi di Milano)
- Alessia Galdeman (Università degli Studi di Milano)
- Manuel Dileo (Università degli Studi di Milano)
- Christian Quadri (Università degli Studi di Milano)
- Matteo Zignani (Università degli Studi di Milano)
- Sabrina Gaito (Università degli Studi di Milano)

Big Data Efficiency and Reliability (Presentation)

Chair:

Angela Bonifati, Lyon 1 University & CNRS

Program

Differential Subsampling for Streaming Data
- Antonio Maratea (University of Naples Parthenope)
- Angelo Ciaramella (University of Naples Parthenope)
- Rita Perna (University of Naples Parthenope)

Move Big Data processing at the edge through network aware capabilities
- Domenico Garlisi (University of Palermo)
- Gabriele Restuccia (University of Palermo)
- Ilenia Tinnirello (University of Palermo)
- Roberto Pirrone (University of Palermo)

Large-scale Graph Analysis of IoT Data
- Lorenzo Goglia (University of Sannio)
- Carmine Colarusso (UniveUniversity of Sannio)
- Angelo Furno (ENTPE, University Gustave Eiffel, LICIT)
- Eugenio Zimeo (University of Sannio)

Safe and Reliable Data Science: are we ready for prime-time ?
- Danilo Franco (University of Genoa)
- Luca Oneto (University of Genoa)
- Davide Anguita (University of Genoa)

Information Extraction from Big Data (Presentation)

Chair:

Paolo Ceravolo, Università degli Studi di Milano

Program

Extracting Information from Large Amounts of Investment Product Documents
- Federico Maria Scafoglieri (University of Rome Sapienza)
- Alessandra Monaco (University of Rome Sapienza)
- Giulia Neccia (University of Rome Sapienza)
- Domenico Lembo (University of Rome Sapienza)
- Alessandra Limosani (Consob)
- Francesca Medda (Consob)

From Text to Data: Extracting Structured Information and Querying it via KGQA and BESt Queries
- Maurizio Atzori (University of Cagliari)

Simplicial Information System
- Emanuela Merelli (University of Camerino)
- Matteo Rucco (Biocentis)

A Machine Learning Model for Inferring Gene Regulatory Networks
- Francesco Zito (University of Catania)
- Vincenzo Cutello (University of Catania)
- Mario Pavone (University of Catania)

Big Data Processing and Integration (Presentation)

Chair:

Valerio Bellandi, Università degli Studi di Milano

Program

Nondiscrimination-aware Data Processing Pipelines
- Chiara Accinelli (University of Genoa)
- Barbara Catania (University of Genoa)
- Giovanna Guerrini (University of Genoa)

Big Data Integration for Data-Centric AI
- Sonia Bergamaschi (University of Modena and Reggio Emilia)
- Domenico Beneventano (University of Modena and Reggio Emilia)
- Giovanni Simonini (University of Modena and Reggio Emilia)
- Luca Gagliardelli (University of Modena and Reggio Emilia)
- Adeel Aslam (University of Modena and Reggio Emilia)
- Giulio De Sabbata (University of Modena and Reggio Emilia)
- Luca Zecchini (University of Modena and Reggio Emilia)

Domain-Specific Big Data (Presentation)

Chair:

Claudio A. Ardagna, Università degli Studi di Milano

Program

Bridging the gap between Big Data and Blockchain: the Food Certification case study
- Ada Bagozi (University of Brescia)
- Devis Bianchini (University of Brescia)

A 5G-IoT enabled big-data infrastructure for data-driven agronomy
- Filippo Berto (University of Milan)
- Marco Torrente (University of Milan)
- Daniele Manenti (University of Milan)
- Cristina Fra' (TIM S.p.A.)
- Luca Ciani (TIM S.p.A.)
- Enrico Ferrari (University of Milan)
- Aldo Calcante (University of Milan)
- Roberto Oberti (University of Milan)

TOREADOR Heritage

EU H2020 Project “TrustwOrthy model-awaRE Analytics Data platfORm (TOREADOR) was the first project coordinated by CINI and was carried out under the umbrella of the National Lab on Big Data. It aimed to overcome major hurdles that have prevented many European companies from reaping the full benefits of Big Data analytics (BDA), namely, the lack of IT expertise and budget. To overcome this hurdle, TOREADOR proposed a model-based BDA-as-a-service (MBDAaaS) approach, providing models of the entire Big Data analysis process and of its artefacts. TOREADOR open, suitable-for-standardisation models support substantial automation and commoditisation of Big Data analytics, while enabling it to be easily tailored to domain-specific customer requirements.

This tutorial presents the heritage left by TOREADOR in the business and research community as a whole. It then presents how some of its partners exploited the project results and knowledge in their business processes and supply chains as follows.

Engineering Ingegneria Informatica presents how starting from the TOREADOR approach and results, it has been possible to advance towards a cloud-edge Data Science and Machine Learning platform for the rapid Big Data Analytics (BDA) application prototyping and deployment on the edge. During the session it will be presented a real use case, under development in the INFINITECH project, where such a platform is used to design and develop a ML-based system on Cybersecurity and Fraud Detection in Financial Transactions.
SAP Research presents the impact of the TOREADOR approach on SAP business processes and their research activities
AVIO AERO presents the impact of the TOREADOR approach on AVIO business processes.
CINI presents the impact of project TOREADOR on the activities of Sesar Lab at Università degli Studi di Milano along three main lines: i) Big data platform configuration and deployment; iii) research on big data assurance and governance.

Presenters:

Davide Michele Bezzi, SAP Research, France
Davide Dalle Carbonare, Engineering Ingegneria Informatica, Italy
Salvatore Cipolla, Engineering Ingegneria Informatica, Italy
Roberto Merotto, Avio Aero Italy
Alberto Riccabone, Avio Aero, Italy
Claudio A. Ardagna, Università degli Studi di Milano, Italy
Nicola Bena, Università degli Studi di Milano, Italy
Jonatan Maggesi, Università degli Studi di Milano, Italy
Antongiacomo Polimeno, Università degli Studi di Milano, Italy

SoBigData.eu: A Research Infrastructure to Empower Data Science Analysis

Abstract of the tutorial:

The SoBigData RI's service platform empowers researchers for the design and execution of large-scale social mining experiments. Pushing the FAIR (findable, accessible, Interoperable, responsible) and FACT (Fair, Accountable, Confidential and Transparent) principles, the RI renders social mining experiments more efficiently designed, and repeatable by leveraging concrete tools that operationalize ethics, incorporating values and norms for privacy, fairness, transparency and pluralism; also touching upon how data science helps us to make more informed choices, underlining the need to achieve collective intelligence without compromising the rights of individuals.The tutorial will show the services made available by the RI and will focus on the computational resources provided to the users in the SoBigData virtual laboratory. Examples of usage of the SoBigData libraries and its method engine will be presented, and the users will be able to follow and repeat the experience on the SoBigData Lab.

Organizers:

Roberto Trasarti , CNR, Italy

Short Bio:
Roberto Trasarti is a member of ISTI-CNR, and also a member of Knowledge Discovery and Delivery Laboratory. Currently the coordinator of SoBigData++ project. His interests regard Data mining, Spatio-Temporal data analysis, Artificial intelligence, Automatic Reasoning.
Valerio Grossi , CNR, ISTI, KDD Lab, Italy

Short Bio:
Valerio Grossi holds a Ph.D. in Computer Science from the University of Pisa and is part of the Knowledge Discovery and Data Mining Laboratory. He is the project manager of the SoBigData++ project, and his research interests focus on the analysis of massive and complex data, including mining data streams, ontology-driven mining, business intelligence, and knowledge discovery systems.
Giulio Rossetti , CNR, ISTI, KDD Lab, Italy

Short Bio:
Giulio Rossetti is a member of the Knowledge Discovery and Data Mining Laboratory, a joint research team that connects the Computer Science Dept. of the University of Pisa and the ISTI-CNR. His research activity centers on the definition of algorithms for complex network analysis and data science.
Francesca Pratesi , CNR, ISTI, KDD Lab, Italy

Short Bio:
Francesca Pratesi is a member of the Knowledge Discovery and Data Mining Lab. Her research interests include data mining, data privacy and privacy risk assessment, mainly in spatio-temporal data. Recently, she broadened her interest, moving towards the Ethics-by-Design paradigm and Trustworthy AI.
Beatrice Rapisarda , CNR, IIT, Italy

Short Bio:
Beatrice Rapisarda is the Head of Internal and External Communication for SoBigData++ project. She is responsible for the design and production of material to support communication and promotion in the scientific / technological field (websites, brochures, presentations, posters, videos, newsletters, etc.). She is also part of the organization for events relating to research activities and scientific dissemination for SoBigData++.

Program:

SoBigData RI Ecosystem and Services, duration: 30 minutes;
Hands-on JupyterHub service and SoBigData Libraries, duration: 45 minutes;
Responsible Data Science: FAIR: an E-learning module for GDPR compliance and ethical aspects, duration: 15 minutes;
Open discussion with participant, duration: 30 minutes;

Studying Fake News Spreading, Polarization Dynamics, and Social Media Manipulation

Abstract of the tutorial:

This tutorial's objectives are supposed to provide

A general understanding of the main research problems and most relevant findings in the new "science of fake-news" multidisciplinary sub-field, focusing on a selection and unavoidably biased list of some of the most influential and related scientific papers that have been published so far, especially in the last 7 years;
An introduction of the underlying mechanisms that make fake news propagation fast and difficult to stop (such as homophily, segregation and polarization in social networks, belief reinforcements, simple vs complex social contagion, and so on);
The main methodologies used for understanding the phenomenon, as well as some of the techniques adopted so far to try to limit the spreading of low quality information (and why they basically fail in the short term). These methodologies are based on deterministic as well as not deterministic modeling that allow what-if analyses as well as on data-driven approaches based on empirical observations;

Although the set of problems and methodologies is naturally multidisciplinary, the main field category is computer science; nevertheless, anyone willing to make data science their own field of expertise, would be potentially interested in this topic.

Organizers:

Giancarlo Ruffo , Università degli Studi di Torino, Italy

Short Bio:
Giancarlo Ruffo, Ph.D, is Associate Professor of Computer Science at the University of Turin, Italy from 2006, and Adjunct Professor at Schools of Informatics and Computing from 2011, Indiana University. He has been ISI fellow (awarded by ISI Foundation) from 2015 to 2018, and member of the Board of Directors of SAA - Scuola di Amministrazione Aziendale (the MBA School at the University of Turin) from 2015 to 2017. Additionally, he has also been the coordinator of the master's degree program in "Networks and Computational Systems" (Reti e Sistemi Informatici) at the University of Turin.
His current research interests fall in the multidisciplinary research area of Computational Social Science and Network Science, with focus on data visualization and data-driven approaches to model the diffusion of misinformation, opinion polarization in social media. He also investigated research problems on web and data mining, recommendation systems, social media, distributed applications, peer-to-peer systems, security, and micropayment schemes. He is the principal investigator of the ARCS group, and he has led several research projects.
He has published about 80 peer-reviewed papers in international journals and conferences. He has more than 20 years of teaching experience, and currently he teaches a course on “Web technologies” for the bachelor’s degree program in Computer Science, and a course on “Complex Networks Analysis and Visualization” for both the master’s degree program in Computer Science and the master’s degree program in Stochastics and Data Science at the University of Turin.
Aside from his academic work, he has been involved in many other professional activities as free-lance consultant in the last 25 years. In 2013 he co-founded NetAtlas s.r.l., a tech company specialized in mobile application development, social media services and data fusion platforms design.

Home EnergIA - An Engineering Ingegneria Informatica's Solution for NILM

Abstract of the tutorial:

The industrialization process, which took place mainly in the last two centuries, has led to an improvement in lifestyle, products, services, but also in the consumption of electricity. Most of the final consumption in industrialized and developing countries is due to the residential sector. In Italy, for example, the civil sector absorbs about one third of final consumption. Controlling the way of consuming is the first step in planning and carrying out interventions that minimize it. Non-Intrusive Load Monitoring is a way for homeowners and building managers to monitor device-level energy consumption without having to install multiple dedicated sensors in an entire home or office building. This is not a new concept, but only recently there are systems that give commercially viable solutions. HomeEnergIA is Engineering's system that offers an original and accurate solution that detects consumption in homes with a single sensor placed on the meter and transmits aggregate data in real time to remote systems. With powerful AI algorithms, these disaggregate the signal identifying the devices, when they turn on, when they turn off and how much they consume. The results and their analysis are displayed on the web portal and on mobile applications, both for the use of the service operator and the end user. During the tutorial/demo this solution will be illustrated.

Organizers:

Marco Breda , Engineering Ingegneria Informatica

Short Bio:
Marco Breda is currently Director of the Advanced Analytics & AI Area of the Data & Analytics Center of Excellence at Engineering Ingegneria Informatica, where, with machine learning paradigms, complex analytical systems are designed and built for public and private industrial companies in all the main ICT sectors. Before joining Engineering in 2009, he has worked as a Data Warehouse, Business Intelligence and Data Mining project manager in various IT companies since 1998. Previously he worked in Ericsson Telecomunicazioni's Research Division, since 1991, dealing with Broadband Networks and Complex Systems. And before that, since 1990, in TIM, designing advanced private networks for large users. In parallel, since 1997, he is working as an Associate Researcher at the Semeion Research Center, mainly involved in basic research activities in Artificial Intelligence, producing about thirty publications. Since 2010 he is a lecturer at the "Enrico della Valle" School, giving courses on Data Warehousing and Advanced Analytics. He received a M.Sc., summa cum laude, in Electronic Engineering in 1989, from the "Sapienza" University of Rome.
Franco Ermellino , Engineering Ingegneria Informatica

Short Bio:
Franco Ermellino is currently Director of Innovation for Energy & Utilities BU at Engineering Ingegneria Informatica. He manages the process and monitors the progress of the various ongoing innovative initiatives about ‘energy’; he builds out innovation capabilities and transfers them into the BU culture to impact the broader organization. He’s owner of several Engineering innovative products and services, included a worldwide patented solution for energy roaming, the Mobile Energy. Among the most relevant initiatives, he leads the Home EnergIA platform, the Engineering response to the NILM needs. More than 20 customers are currently using Home EnergIA as the building block of their services. He has been in Engineering for 24 years, always realizing innovative solutions and platforms, 14 years in Telco market, and last 10 years moving his innovation attitude towards energy services. Before joining Engineering in 1998, he was researcher at the University of Salerno, where he realized a first version of Google Maps (for Salerno city) in 1996. He received a M.Sc., summa cum laude, in Computer Science Engineering in 1996, from the University of Salerno.
Felice Tuosto , Engineering Ingegneria Informatica

Short Bio:
Felice Tuosto is currently Chief Data Scientist of the Advanced Analytics & AI Area of the Data & Analytics Center of Excellence of Engineering Ingegneria Informatica, where, with machine learning paradigms, complex analytical systems are designed and implemented for public and private industrial companies in all main ICT sectors. Since 2006 he has been working on advanced custom white-box, black-box and gray-box models in order to solve unique, real and complex problems: his goal is to combine this knowledge together with some traditional and advanced programming practices. He has experience and knowledge of power systems, computer vision, natural language processing, anomaly detection, time series prediction, graphical neural networks, speech / audio analysis, generative adversarial networks and so on, with Python C and C++. He leads not only business projects, but also research and development tasks, document and source code writing. Since 2019 he has been a lecturer at the “Enrico della Valle” School, holding courses in data mining, machine and deep learning. He received his Master's Degree in Automation Engineering in 2006 at the University of Sannio in Benevento.

What is the shape of a neural circuit? Data, featured network and neurons.

Abstract of the tutorial:

In neuroscience, the structural connectivity matrix of synaptic weights between neurons is one of the critical factors determining the overall function of a network of neurons. The mechanisms of signal transduction have been intensively studied at different time and spatial scales and at both the cellular and molecular level. While a better understanding and knowledge of some basic processes of information handling by neurons has been achieved, little is known about the organization and function of complex neuronal networks. Experimental methods are now available to simultaneously monitor electrical activity of a large number of neurons in real time. Here, we present a methodology to infer the connectivity of a population of neurons from their voltage traces. At first, putative synaptic events were detected. Then, a multi-class logistic regression was used to fit the putative events to the spiking activities and a penalization term allowed to regulate the sparseness of the inferred network. The proposed weighted Multi-Class Logistic Regression with L1 penalization (MCLRL) was benchmarked against data obtained from in silico network simulations. MCLRL properly inferred the connectivity of all tested networks, as indicated by the Matthew correlation coefficient (MCC). Importantly, MCLRL also accomplished to reconstruct the connectivity among subgroups of neurons randomly sampled from the network. The robustness of MCLRL to noise was also assessed and the performances remained high (MCC>0.95) even in extremely high noise conditions (>95% noisy events). We also devised a data driven procedure to gather a proxy of the optimal regularization term, thus envisioning the application of MCLRL to experimental data Finally, the performances of MCLRL were optimal even with small samples of network activity (5 to 10 seconds), which is again relevant for applications. The optimal network inferences obtained with MCLRL substantiate the interest in approaches inspired by the novel ideas coming from physics-informed machine-learning field.

Organizers:

Thierry Nieus

Short Bio:
Thierry Nieus earned a PhD in Applied Mathematics in 2004 working on computational models of of neuronal cells. During his PhD he spent a period abroad in Belgium and France collaborating at the EU projects Cerebellum and Spikeforce. He used information theory tools to quantify the processing of the inputs by in silico and experimentally recorded neural cells. At the fall of 2006, he moved to the Italian Institute of Technology (IIT) working on detailed models of synaptic dynamics. At the IIT he also started working on designing data analysis pipelines to process large scale recordings of brain tissues gathered with cutting-edge technologies. During his stay at the IIT he has acquired a good knowledge of the cellular and sub-cellular processes determining neuron’s activities, of the mathematical and computational approaches used for modeling and of the machine learning, graph theory and information theory tools to investigate the underlying computation. In 2016, he moved to the iTCF laboratory headed by Marcello Massimini (Università degli Studi di Milano - Italy) working on entropy based measures to quantify the complexity of brain signals in humans. He also brought these measures to cell culture networks, cerebellar brain slices and computational models. He has a longstanding experience in teaching computational neuroscience, computer programming and data analysis tools to undergraduate and PhD students. He also supervised the research activity of 4 PhD students. At the fall of 2021, he moved to the HPC Indaco Unitech (Università degli Studi di Milano – Italy), where he is involved in teaching the basics of HPC as well as in data science and neuroscience projects with private companies and research groups.
Daniele Borgonovo
Shyam Diwakar
Giacomo Aletti
Giovanni Naldi

Francesco Bonfiglio - CEO, Gaia-X

Topic: Gaia-X: federating dataspaces via an open-source architecture framework

Short Bio:

Francesco Bonfiglio joined GAIA-X in March 2021 as the new CEO.
With more than 30 years' experience in the business of consulting and information technology, Francesco brings his knowledge of the cloud and data market needs, and his vision on how Gaia-X should revolutionize it. Independent Advisor since 2020, he has spent his previous professional life as an executive in many Italian and Multinational contexts. Formerly Chief Executive Officer for ENGINEERING D.HUB (Hybrid Cloud and Digital Transformation company within the largest Italian System Integrator Group), Vice President for Technology Services at AVANADE and Managing Director within the ACCENTURE Group, Chief Technology Officer for HEWLETT-PACKARD EMEA, SW Factory Director at UNISYS, Technical Director and Methodology Evangelist at RATIONAL SOFTWARE amongst his positions.
With a background in Electronics Engineering, he started his career in the late 80s as a HW/SW Engineer working on some of the most advanced Military R&D projects, from the Sonar System for the Italian Navy (ANSQQ14IT) prized during the Gulf war for the mine-hunting performances, to the Eurofighter Fighter Aircraft (EFA), still one of the most advanced examples of technology in the Military field.
Francesco had a bottom-up career, covering all roles within the IT, from developer to top-executive, from R&D to business units' leader. An early supporter of the need for continuous innovation, he is a big fan of the startup ecosystem, active member of evaluation commissions, and being board member and co-founder of FOOLFARM.com, the first example of Startup Studio in Italy.
Amongst other roles Francesco has represented the IT world and then became Vice President of Confindustria (Italian Trade Association) for Valle D'Aosta.
Living in Italy, in the countryside out of Milan, married and father of three sons, he loves music and played in several independent live and record productions since the 80s.
Francesco believes in the power of collective intelligence, lateral thinking, and teamwork, as a propeller for transformation, in the business as well as in the whole life!

Abstract

Gaia-X represents the next generation of data infrastructure ecosystem: an open, transparent, and secure digital ecosystem, where data and services can be made available, collated, and shared in an environment of trust. The architecture of Gaia-X is based on the principle of decentralization.

Gaia-X is the result of many individual data owners (users) and technology players (providers) - all adopting a common set of policy, technical, and labelling rules and specifications - the Gaia-X framework.

Together, we are developing a new concept of data infrastructure ecosystem based on the values of openness, transparency, sovereignty, and interoperability, to enable trust. What emerges is not a new cloud physical infrastructure, but a software federation system that can connect several cloud service providers and data owners together to ensure data exchange in a trusted environment and boost the creation of new common data spaces to create digital economy.

In a digital market that sees a cloud market growing threefold in three years, between 2017 and 2020, EU CSP providers have seen a drop in their share (from 26% to 17%) and the dominance of a handful of dominant players taking the lead. On the other side the enormous opportunity represented by data exploitation is still at a starting point, with 80% of industrial data untapped in on premises systems, limited data sharing, and no common dataspaces within and across sectors. The future of data economy is at stake, and with it the future of our continent and lives. The EU's aspiration for a new era of digital innovation requires a new paradigm of digital trust to enable the creation of common dataspaces at scale, and a new concept of data infrastructures and digital services, transparent, controllable, and interoperable, to bring control back from technology providers to technology users. The scope of the presentation is to give an overview for the core concepts behind the Gaia-X Framework, the benefits of it, and its' business value and respective applicability.

About Gaia-X

Gaia-X brings together companies, associations, and research institutions to create a trustworthy digital ecosystem. Their international project is designed to simplify collating and sharing data by creating dedicated data spaces to serve critical industries. Most importantly, the users retain sovereignty over their data. With over 1,800 participants, Gaia-X has created a federated system to link cloud users together. For more information, visit www.gaia-x.eu.

Quote

“...Data is a new gold, owned by each and every one and democratically distributed all over the globe. This is a unique and unprecedented opportunity for the human being to build a new economy with greater and more equal opportunities for any data provider or consumer in any country. The participation to GAIA-X by hundreds of partner companies, witnesses the common need for a new platform of rules and technologies, that can make concepts like data sovereignty, interoperability and transparency, truly concrete, tangible and measurable. This is the objective of GAIA-X, the success of which will mark an irreversible boost for Digital Economy, not just for Europe. As an IT professional and as a European citizen I'm honored and thrilled by the opportunity to give my contribution to a project that can shape a better future, not for the IT sector per se, but for the entire economy of our countries and for the life of us and our loved ones...”

Stefano Ceri - Full Professor, Politecnico di Milano

Topic: Storing and Analyzing Viral Sequences through Data-driven Genomic Computing

Short Bio:

Stefano Ceri is a professor of Data Management at Politecnico di Milano. His main research interests are extending data management and then acting as data scientists in numerous domains - including social analytics, fake news detection, genomics for biology and for precision medicine, and recently studies concerning the SARS-CoV-2 viral genome. He is the recipient of two ERC AdG, “Search Computing” (2008-2013) and “data-driven Genomic Computing” (2016-2021). He received the ACM-SIGMOD "Edward T. Codd Innovation Award" (June 2013). He is an ACM Fellow.

Abstract

Prof. Ceri will give a simple and data-inspired illustration of what is a viral sequence, what are mutations, how mutated sequences become organized forming a “variant”, what are the effects of individual mutations and of variants. Then, Prof. Ceri will illustrate the process of deposition of viral sequences to public repositories (GenBank, COG-UK, GISAID). In the second part of the seminar, Prof. Ceri will discuss the systems that were developed within his group, thanks to ERC and EIT funding and to the availability of big data collections. He will briefly introduce the data model and integration system that allowed to collect and store SARS-CoV-2 sequence data. Several systems were built on top of such data collection. Specifically, Prof. Ceri will illustrate: (i)ViruSurf, a search system enabling free metadata-driven search over the integrated and curated databases; (ii) EpiSurf, a tool for intersecting viral sequences with epitopes - used in vaccine design; (iii) VirusViz, a data visualization tool for comparatively analyzing query results; (iv) ViruClust, a tool supporting data aggregation and groups comparison; (v) VariantHunter, a tool for observing interesting variant trends and identifying novel emerging variants. If time allows, Prof. Ceri will also discuss an example of data analysis for revealing variants.

PROGRAM

Legend

Day 1

Day 2

Instructions for presenters