Big Data

Aims:

The Big Data Section is dedicated to the foundational architectures, systems, and methodologies that enable the acquisition, storage, management, and processing of massive-scale datasets. This Section aims to publish research that addresses the significant challenges of volume, velocity, variety, and veracity in the big data lifecycle. We focus on innovative solutions that enhance the performance, scalability, reliability, and security of big data infrastructures, bridging the gap between theoretical models and practical, real-world deployments. The Section serves as a forum for discussing the entire data pipeline, from ingestion and integrity to efficient processing and application across diverse sectors.

Scope:

This Section covers the engineering and systemic aspects of big data. Key topics include the following:

• Big Data Models and Architectures: Novel computational and programming models (e.g., MapReduce, Spark), distributed and parallel architectures.
• Big Data Infrastructure and Systems: Design and implementation of data centers, cloud-based data systems, high-performance computing (HPC) for data analytics, and storage technologies.
• Data Integrity, Privacy, and Security: Techniques for ensuring data quality, provenance, anonymization, and secure access control in large-scale environments.
• Big Data Applications: Real-world case studies and frameworks in science, finance, telecommunications, healthcare, smart cities, and industrial manufacturing.
• Data Storage and Management: Databases (SQL, NoSQL, NewSQL), data warehousing, data lakes, and stream processing systems.
• Internet of Things (IoT) and Sensing Systems: Architectures and platforms for collecting and handling data streams from IoT devices and sensor networks.