Editorial:
"Theory and Practice in Data Analysis,
Machine Learning, and Visualization"
and
"The Future of Digital Transformation
in Business Processes"

William Steingartner
We are preparing this special issue to present recent advances in informatics that bridge theoretical concepts with practical, data-driven methodologies. The contributions included in this issue reflect current research trends in signal processing, machine learning, data analysis, and visualization, addressing challenges that arise when working with complex, noisy, and high-dimensional data.
The special issue brings together works that emphasize both methodological rigor and applicability to real-world problems, ranging from human–computer interaction and biomedical signal analysis to exploratory data analysis and correlation modeling. By combining perspectives from applied machine learning and graph-based data visualization, the issue aims to highlight innovative approaches to extracting meaningful information from data and to stimulate further research at the intersection of theory, experimentation, and application.
After careful consideration, we invited experts whose research aligns with this vision. The resulting contributions demonstrate diverse yet complementary approaches to modeling, analyzing, and interpreting complex data, underscoring the interdisciplinary character of contemporary informatics research.

Mirjana Radović-Marković:
This special issue aims to showcase the latest advances and future directions in informatics, featuring cutting-edge research across diverse areas of computer science. It provides a platform for exploring both theoretical concepts and practical implementations, fostering collaboration and the sharing of insights.