Scope and topics

The digital transformation is inextricably influencing our lives today. The constant flow of data and technological innovations have reshaped the way we live. The advent of technologies such as cloud computing, the Internet of Things, and Artificial Intelligence (AI) has given rise to an interconnected digital ecosystem, where billions of devices generate an enormous amount of data and information. This "data explosion” is much more than a mere technological challenge. It is a true revolution that is profoundly affecting our society.   

Data Science holds the key to tame this revolution. It allows to make the most of this huge amount of data, creating concrete value out of it while enabling new, smart processes and applications. Through sophisticated algorithms and advanced analysis techniques, data science allows to extract knowledge from this mass of raw data, reveal hidden patterns, predict future trends, and support sound decision-making. Data science promises, and is already delivering, unmatched efficiency and efficacy touching every sector of our society, from smart cities that optimize traffic and resource management, to intelligent transportation systems that improve safety and efficiency, to digital healthcare that promises more accurate diagnoses and personalized treatments. In the industrial sector, the analysis of data from production lines optimizes processes, reduces waste, and improves quality. In finance, machine learning algorithms predict market trends, assess credit risk, and prevent fraud. Data science is thus becoming the engine of progress, opening new frontiers and offering innovative solutions to complex problems.

However, this data-driven revolution comes with new challenges. On the one hand, security and privacy, as well as additional non-functional requirements, must be enforced along the entire data- driven pipeline, from data ingestion to AI model deployment, demanding a rethink of the traditional CIA (Confidentiality, Integrity, Availability) triad. On the other hand, a whole new set of (legal) requirements emerge, starting from the ethical implications of AI, to ensure that data-driven technologies are used responsibly and for the benefit of the society as a whole. In addition, a responsible usage of these technologies also implies the usage of large computing infrastructures in a sober, environment-friendly manner.  

In a nutshell, the data-driven ecosystem represents an extraordinary opportunity for progress and innovation. This means that a multidisciplinary approach is needed to fully exploit its potential, integrating technological, scientific, and ethical skills. This is the only way to build a future in which data are at the service of humanity, contributing to creating a just, sustainable, and prosperous society.

The topics of interest for this special session include (but are not limited to):

• Data Architectures

• Data integration and interoperability

• Data processing, analytics and visualization

• Data quality and utility

• Data shaping, modeling and design

• Data sovereignty and governance

• Data storage, preparation and operation

• Data trustworthiness and ethics

• Open science and open data

• Process mining and business intelligence

• Multimodal data processing

• Deep Neural Networks

• Artificial Intelligence

• Generative AI

• Symbolic Computing

• AI security and security for AI

• Cloud-edge continuum

Submission Guidelines

Please follow the submission guideline from the IJCNN2025 submission website. To submit a paper use the CMT website. Manuscripts related to the Special Session must be submitted as a regular paper (Main Track) by selecting this special session “Data Science: Multidisciplinary Perspectives to Tame the Data Revolution” as primary Subject Area. All submitted papers will be reviewed in the same process as the regular papers. Accepted contributions will be part of the conference proceedings.

All the accepted and presented papers will be published on IEEE Xplore Digital Library and indexed by Scopus.

Note that anonymizing your paper is mandatory, and papers that explicitly or implicitly reveal the authors' identities may be rejected.

Important Dates

January 30th January 15th, 2025 - Paper Submission Deadline (extended)

March 31st, 2025 - Paper Acceptance Notification

May 1st, 2025 - Camera-Ready Paper Submission Deadline

May 1st, 2025 - Early Registration Deadline

Organizers

Nicola Bena
University of Milan

Università degli Studi di Milano. Nicola Bena is a postdoc at the Department of Computer Science, Università degli Studi di Milano, where he obtained the Ph.D. in Computer Science in 2024. His research interests are in the area of security of AI and distributed systems, with particular reference to certification, assurance and risk management techniques. He has published 20 contributions in international journals and conference/workshop proceedings. He has been visiting scholar at Khalifa University and INSA Lyon. He has been PC member of 3 conferences/workshops and TPC member of more than 45 conferences and workshops.

Emanuel Di Nardo
University of Naples Parthenope

Emanuel Di Nardo received his Ph.D. from University of Milan, Italy, in 2022 with a focus on Computer Vision and with a deep study in neural networks modelling for Visual Object Tracking. He is an Assistant Professor in Computer Science at the Department of Science and Technologies, University of Naples Parthenope and member of the Computational Intelligence & Smart Systems Lab. Further, he worked as consultant on Artificial Intelligence for private companies. His main interests are neural networks modelling for Computer Vision applications. His interests, also, spread on multiple fields, from generative methodologies to building deep neuro-fuzzy architecture to application in interdisciplinary fields like Marine Science, Biology and Medical Applications, with a deep focus on explainable AI. He is a member of IEEE, AIxIA, SIREN and CVPL associations.

Angelo Ciaramella
University of Naples Parthenope

Angelo Ciaramella received, in 1998, the Laurea degree (cum laude) in Computer Science from the University of Salerno and in 2002 he received a Ph.D. from the same University. From 2021 he is Full Professor at the Department of Science and Technology of the University of Naples “Parthenope” where he serves as President of the Course of Study in Computer Science (informatica.uniparthenope.it), Director of the Apple Foundation Program Parthenope (iosdeveloperacademy.uniparthenope.it), Head of the Computational Intelligence & Smart Systems Lab (cisslab.uniparthenope.it), Director of the local nodes of the CINI Big Data, Digital Health and InfoLife national laboratories. The main research interests of Angelo Ciaramella are Computational Intelligence, Machine Learning and Data Mining. In particular, he has been interested in statistical, Machine Learning and Deep Learning approaches for Blind Source Separation, Sparse Coding, Compressive Sensing and Dictionary Learning, for signal processing (i.e., audio, streaming, astrophysical and geological) and feature extraction. He has been working on fuzzy and neuro-fuzzy systems for structured and unstructured data. He is interested in developing Fuzzy Decision Support Systems in risk assessment. He also studied and developed new methodologies for pre-processing, clustering, visualization, and assessment of biological, air quality and social network data (e.g., twitter). He is also interested in signal processing by Deep Learning methodologies in Brain Computer Interfaces. He is associate editor of international journals (i.e., Information Sciences) and are editor of Soft Computing Journal, he has been co-editor of books and guest editor of Special Issues. He is in the steering committee of WILF conference, has been co-general Chair (ITADATA2023, PDP2023, WILF2021, IDCS2019), technical chair (CIBB2018), organizer and chair of Special Sessions (e.g., EAIS, CIBB, WIRN, Fuzz-IEEE, NAFIPS), and he is in the Program Committee (e.g., CIBB, EAIS, Fuzz-IEEE, WIRN, GCIS, ICIC, AI2IA) of international conferences.  He is a Senior Member of IEEE and member of IEEE Computational Intelligence Society, IEEE Signal Processing, SIREN, GIRPR and AIxIA.

Claudio A. Ardagna
University of Milan

Claudio Ardagna is Full Professor at the Department of Computer Science, Università degli Studi di Milano, the Director of the CINI National Lab on Data Science, and co-founder of Moon Cloud srl. His research interests are in the area of cloud-edge and AI security and assurance, and data science. He has published more than 170 contributions in international journals, conference/workshop proceedings, and chapters in international books. He has been visiting professor at Université Jean Moulin Lyon 3 and visiting researcher at Beijing University of Posts and Telecommunications, Khalifa University, George Mason University. He is member of the Steering Committee of IEEE TCC, member of the editorial board of the IEEE TCC and IEEE TSC, and secretary of the IEEE Technical Committee on Services Computing.