Towards Digital Intelligence Society: A Knowledge-based Approach

A Scholarly Look at Digital Intelligence

As digital technologies become embedded in nearly every aspect of modern life, the challenge is not only to process more data but to do so intelligently. This volume brings together a carefully selected collection of nine chapters that explore knowledge-based approaches to building a smarter digital society. Each chapter was peer-reviewed and refined to present current research with practical relevance.

Three Complementary Perspectives

The book is structured around three core themes:

• Digital Intelligence: Selected Research Challenges – Addressing societal and technological hurdles, from detecting antisocial behavior online to advanced computer vision and human-machine collaboration.
• Intelligent Analysis of Big Data – Covering knowledge-driven data analytics, smart grid optimization, and astronomical time series classification using evolutionary neural networks.
• Data and Information Modelling – Exploring Industry 4.0 knowledge management, formal concept analysis for computational thinking, and database join optimization without foreign key indexes.

Who Will Find This Volume Useful

Researchers, graduate students, and practitioners in artificial intelligence, data science, and knowledge engineering will appreciate the blend of theoretical foundations and applied case studies. The chapters provide both conceptual frameworks and experimental results, making the volume a valuable reference for those working on intelligent systems that rely on knowledge extraction and modeling.

From the Editors

The editors—Ján Paralič, Peter Sinčák, Pitoyo Hartono, and Vladimír Mařík—are active researchers in cybernetics, AI, and intelligent systems. They have organized the contributions to highlight how knowledge can be explicitly modeled or implicitly leveraged to solve complex problems across domains such as social media, energy, astronomy, and industry.