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Welcome to Intelligent Data-Intensive Systems (ID-IS)
In today’s data-rich environment, the production and utilization of large data volumes are ubiquitous. Multiple scientific domains, such as environmental sciences and remote sensing, generate vast datasets that are invaluable across various fields. The ability to harness these datasets effectively is becoming increasingly important for driving scientific research and innovation.
Leveraging AI Across Diverse Domains
Despite the recent spotlight on AI in computer science applications like computer vision, AI solutions are underutilized in domains such as the Natural Sciences. By employing AI methods in these fields, complex datasets can be abstracted into representative patterns and simpler latent representations. This not only simplifies the data but also optimizes complex workflows for efficient cloud computing, leveraging AI to uncover hidden knowledge and enhance performance.
The Rise of Data-Intensive Research
In an era where data is abundant and readily accessible, data-intensive research and applications are gaining traction. Discovering knowledge from voluminous datasets through advanced visualization, statistical analysis, and machine learning is becoming increasingly crucial. Advancements in “Next-generation data engineering,” including Information Fusion, are driving better performance in identifying and utilizing representative patterns, making data more actionable across various domains.
Promoting Open Science and Reproducibility
Generalized catalogues and vocabularies are essential for enhancing the communication of research advancements. They serve as channels to promote open science and reproducible experiments, methods, evaluations, and results. These tools improve transparency and facilitate the optimization of systems and method development, ensuring that research contributes to broader scientific and technological progress.