Aims and Scope

Aim of JDSIST

The Journal of Data Science, Information Systems, and Technology (JDSIST) aims to provide a peer-reviewed international platform for high-quality research in data science, information systems, and related technologies, fostering the development and dissemination of novel methodologies, data-driven solutions, and system-level innovations that address contemporary technological and societal challenges. The journal encourages interdisciplinary collaboration among data scientists, information system engineers, statisticians, domain experts, and industry practitioners, supporting research that bridges theoretical advances in data science with practical applications in real-world information systems. Committed to transparency, data integrity, reproducibility, and ethical practices, JDSIST publishes original research articles, review papers, case studies, and technical notes that advance data analysis, system design, and information management. By facilitating knowledge sharing and global collaboration, the journal disseminates robust, impactful, and scalable solutions, contributing to evidence-based decision-making, intelligent system design, and technological innovation across academia and industry.

 Scope of JDSIST 

JDSIST welcomes submissions in (but is not limited to) the following broad subject areas:

1. Big data analytics, data mining, and knowledge discovery from structured and unstructured data.
2. Database systems, data warehousing, data management, and scalable data architectures.
3. Data engineering, ETL (extract‑transform‑load), data preprocessing, data quality, and data governance.
4. Machine learning and statistical methods for data analysis, prediction models, clustering, classification, regression.
5. Data science applications in domains like healthcare, finance, smart infrastructure, social analytics, environment, business intelligence.
6. Information system design, development, deployment, and evaluation — enterprise systems, web-based systems, cloud‑based systems.
7. Data-driven decision support systems, business analytics, management information systems, and enterprise resource planning using data insights.
8. Big‑data infrastructure, distributed computing, parallel processing, and scalable system architectures for handling large datasets.
9. Data privacy, security, and ethics in data science and information systems — data protection, secure data sharing, responsible data usage.
10. Data visualization, dashboards, reporting systems, and human–computer interaction for data-driven systems.
11. Real-time data processing, stream analytics, IoT data handling, sensor data management, and time‑series data systems.
12. Data integration, interoperability of heterogeneous data sources, semantic data modeling, and metadata management.
13. Information retrieval, text mining, natural language processing (NLP), and unstructured data analytics in information systems.
14. Evaluation metrics, performance analysis and benchmarking of data systems, scalable algorithms, and system optimization.
15. Data governance, compliance, big‑data policy, and the role of data science in societal and organizational decision‑making.