Florin Pop

Camelia Chira

Oliviu Matei

Petrică Pop-Sitar

This book constitutes the proceedings of the 1st International Conference on Innovative Perspectives on Computational Intelligence and Data Science, InnoComp 2025, held in Cluj-Napoca, Romania, in October 22–24, 2025.

First International Conference, InnoComp 2025, Cluj-Napoca, Romania, October 22–24, 2025, Proceedings, Part I

Innovative Perspectives on Computational Intelligence and Data Science

Massimo Ficco

Francesco Palmieri

Arcangelo Castiglione

This book constitutes the proceedings of the 10th International Symposium on Cyberspace Safety and Security, CSS 2018, held in Amalfi, Italy, in October 2018. The 25 full papers presented in this volume were carefully reviewed and selected from 79 submissions. The papers focus on cybersecurity; cryptography, data security, and biometric techniques; and social security, ontologies, and smart applications.

10th International Symposium, CSS 2018, Amalfi, Italy, October 29–31, 2018, Proceedings

Cyberspace Safety and Security

Catalin Negru

Marta Chinnici

"Many applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. This book will primarily encompass practical approaches that advance research in all aspects of data processing, data analytics, data processing in Cloud/Edge/Fog systems, having a large variety of tools and software to manage them. The book focuses on focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems"--

Data Science and Big Data Analytics in Smart Environments

Gabriel Neagu

This book provides a review of advanced topics relating to the theory, research, analysis and implementation in the context of big data platforms and their applications, with a focus on methods, techniques, and performance evaluation. The explosive growth in the volume, speed, and variety of data being produced every day requires a continuous increase in the processing speeds of servers and of entire network infrastructures, as well as new resource management models. This poses significant challenges (and provides striking development opportunities) for data intensive and high-performance computing, i.e., how to efficiently turn extremely large datasets into valuable information and meaningful knowledge. The task of context data management is further complicated by the variety of sources such data derives from, resulting in different data formats, with varying storage, transformation, delivery, and archiving requirements. At the same time rapid responses are needed for real-time applications. With the emergence of cloud infrastructures, achieving highly scalable data management in such contexts is a critical problem, as the overall application performance is highly dependent on the properties of the data management service.

Case Studies, Methods, Techniques, and Performance Evaluation

Big Data Platforms and Applications

Joanna Kołodziej

Beniamino Di Martino

Serving as a flagship driver towards advance research in the area of Big Data platforms and applications, this book provides a platform for the dissemination of advanced topics of theory, research efforts and analysis, and implementation oriented on methods, techniques and performance evaluation. In 23 chapters, several important formulations of the architecture design, optimization techniques, advanced analytics methods, biological, medical and social media applications are presented. These chapters discuss the research of members from the ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). This volume is ideal as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary works in the areas of intelligent decision systems using emergent distributed computing paradigms. It will also allow newcomers to grasp the key concerns and their potential solutions.

Algorithms, Modelling, and High-Performance Computing Techniques

Resource Management for Big Data Platforms

Ciprian Dobre

This book consists of eight chapters, five of which provide a summary of the tutorials and workshops organised as part of the cHiPSet Summer School: High-Performance Modelling and Simulation for Big Data Applications Cost Action on “New Trends in Modelling and Simulation in HPC Systems,” which was held in Bucharest (Romania) on September 21–23, 2016. As such it offers a solid foundation for the development of new-generation data-intensive intelligent systems. Modelling and simulation (MS) in the big data era is widely considered the essential tool in science and engineering to substantiate the prediction and analysis of complex systems and natural phenomena. MS offers suitable abstractions to manage the complexity of analysing big data in various scientific and engineering domains. Unfortunately, big data problems are not always easily amenable to efficient MS over HPC (high performance computing). Further, MS communities may lack the detailed expertise required to exploit the full potential of HPC solutions, and HPC architects may not be fully aware of specific MS requirements. The main goal of the Summer School was to improve the participants’ practical skills and knowledge of the novel HPC-driven models and technologies for big data applications. The trainers, who are also the authors of this book, explained how to design, construct, and utilise the complex MS tools that capture many of the HPC modelling needs, from scalability to fault tolerance and beyond. In the final three chapters, the book presents the first outcomes of the school: new ideas and novel results of the research on security aspects in clouds, first prototypes of the complex virtual models of data in big data streams and a data-intensive computing framework for opportunistic networks. It is a valuable reference resource for those wanting to start working in HPC and big data systems, as well as for advanced researchers and practitioners.

Florin Pop

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