Aim & Scope

Aim

The Journal of Scalable Data Engineering and Intelligent Computing aims to serve as an national platform for publishing innovative research in scalable data infrastructures, intelligent automation, and computation-driven data engineering. The journal focuses on next-generation architectures and algorithms that enable efficient, automated, and AI-enhanced data processing across enterprise and large-scale distributed environments.

Scope

The journal invites high-quality original research articles, reviews, and case studies in the following areas:

• Scalable data engineering architectures and distributed data systems

• Intelligent automation and AI-driven workflow optimization

• Big data management, storage, and processing frameworks

• Metadata-driven and low-code/no-code data engineering tools

• High-performance ETL/ELT pipeline design and orchestration

• Distributed query engines and large-scale data transformation

• Real-time, streaming, and event-driven data processing

• Intelligent computing techniques for data engineering

• Machine learning–enabled data management and prediction

• Data reliability, quality assessment, lineage, and governance

• Hybrid cloud, multi-cloud, and edge-to-cloud data engineering

• Use cases from enterprise analytics, finance, telecom, healthcare, etc.