About the Journal

ISCSITR - International Journal of Data Mining and Data Warehousing (ISCSITR-IJDMDW) is a reputable, open-access, peer-reviewed journal published by the International Society for Computer Science and Information Technology Research (ISCSITR). Dedicated to advancing the fields of data mining and data warehousing, ISCSITR-IJDMDW provides a platform for researchers, academics, and industry professionals to publish and access high-quality research findings and technological advancements. This journal caters to an international audience, facilitating a broad exchange of knowledge and fostering collaboration across diverse disciplines within computer science, information technology, and related fields.

Aim

The aim of ISCSITR-IJDMDW is to contribute significantly to the body of knowledge in data mining and data warehousing by promoting the publication of cutting-edge research that addresses both theoretical foundations and practical applications. By prioritizing open-access publication, the journal seeks to make innovative data science research widely available, supporting knowledge dissemination and accessibility for researchers and practitioners worldwide. The journal also aims to bridge the gap between academia and industry, promoting studies that offer insights into real-world data management challenges and solutions.

Scope

ISCSITR-IJDMDW covers a broad spectrum of topics within data mining, data warehousing, and related fields. Key areas of focus include, but are not limited to:

  • Data Mining Techniques and Algorithms: Papers discussing innovative approaches to data mining, including clustering, classification, regression, and association rule learning.
  • Big Data Analytics and Applications: Research addressing challenges and advancements in analyzing vast datasets, including big data frameworks, storage, and retrieval systems.
  • Data Warehousing Concepts and Architectures: Studies on efficient data warehouse design, multidimensional data models, ETL (Extract, Transform, Load) processes, and data integration techniques.
  • Machine Learning for Data Mining: Articles exploring the integration of machine learning techniques to enhance data mining, with applications in fields such as finance, healthcare, e-commerce, and social networks.
  • Knowledge Discovery in Databases (KDD): Papers covering techniques for discovering patterns and knowledge in databases, with a focus on predictive and descriptive modeling.
  • Privacy, Security, and Ethical Aspects: Research addressing the ethical implications of data mining and warehousing, including data privacy, security, and regulatory concerns.
  • Applications in Business Intelligence: Studies on how data mining and warehousing support business intelligence initiatives, decision-making, and strategic planning.
  • Data Quality and Preprocessing: Contributions on data quality management, cleaning, preprocessing, and transformation processes to improve data integrity.

By maintaining a rigorous peer-review process and focusing on interdisciplinary research, ISCSITR-IJDMDW seeks to be a vital resource for academics, researchers, and practitioners aiming to stay at the forefront of data science research. The journal’s commitment to open access ensures that valuable insights and breakthroughs in data mining and warehousing are accessible to a global audience, promoting further innovation and development in these rapidly evolving fields.