Aims and scope

Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital. The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains.

Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area. It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering. More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to:

(a) the nature and quality of data,
(b) the computational complexity of data-intensive computing,
(c) new methods for the design and analysis of the algorithms for solving problems with big data input,
(d) collection and integration of data collected from internet and sensing devises or sensor networks,
(e) representation, modeling, and visualization of  big data,
(f)  storage, transmission, and management of big data,
(g) methods and algorithms of  data intensive computing, such as
    mining big data,
    online analysis processing of big data,
    big data-based machine learning,
    big data based decision-making,
    statistical computation of big data,
    graph-theoretic computation of big data,
    linear algebraic computation of big data, and  
    big data-based optimization.
(h) hardware systems and software systems for data-intensive computing,
(i) data security, privacy, and trust, and
(j) novel applications of big data.

Special Issue on Foundation Models for Social Network Analysis
Día de Entrega: 2024-07-31

This special issue seeks to unite researchers in the development of foundation models tailored for Social Network Analysis (SNA). The objective is to enhance the efficiency, accuracy, and understanding of SNA systems, enabling users to navigate and analyze intricate social networks effectively. Anticipated outcomes include improved accuracy in network analysis, efficient extraction of meaningful patterns, and enhanced adaptability for diverse social scenarios. Contributions are invited on diverse aspects of foundation models for Social Network Analysis, encompassing but not limited to: • Novel datasets and benchmarks for building domain-specific foundation models for SNA • Foundation models for predicting social network dynamics • Foundation models for community detection in social networks • Foundation models for sentiment analysis in social media • Foundation models for influence prediction in social networks • Multimodal data analysis with foundation models in the social context • Large-scale data processing with foundation models • Pre-training techniques tailored for social network analysis • Fine-tuning strategies for foundation models • Prompt engineering techniques for foundation models • Transfer learning techniques for SNA foundation models • Domain adaptation techniques for SNA foundation models • Domain-specific foundation models for social networks (e.g., temporal networks, online communities) • Systems and applications based on foundation models in SNA

Special Issue on Data Spaces and Data Governance
Día de Entrega: 2024-12-01

This data revolution provides plenty of opportunities but introduces new gaps and challenges. Data sharing and value extraction happen at an unprecedented scale, with novel requirements on authorization and authentication, data governance, privacy, trust management, latency, to name but a few.

This SI aims to cover the most recent advances in the area of big data and data science towards the creation of virtual (data) spaces, where data can be pooled and shared to maximize utility and trustworthiness while guaranteeing regulatory and legal compliance. These virtual spaces are emerging as a main solution for new-generation data-based applications, but several challenges (target of this SI) remain open from both research and application standpoints. Topics of interest include but are not limited to: ● Frameworks, architectures, and platforms for data governance ● Data modeling and trusted data sharing ● Digital/data sovereignty ● Big data and AI for social good and beyond ● Data governance in high-performance computing ● Ethical data management and analysis ● Novel applications and case studies of data management and governance

Data Science and Engineering is calling for submissions to our collection "Towards the Next Frontier in Data Management: Data Spaces and Data Governance" in conjunction with the best papers accepted from the “3rd Italian Conference on Big Data and Data Science”.