International Journal of Data Warehousing and Mining IGI Global Publishing Impact Factor

International Journal of Data Warehousing and Mining - IGI | 2025 Impact Factor:1.1 | Cite Score:2.8 | SCOPUS Q3

International Journal of Data Warehousing and Mining With Cite Score

Cite Score and Journal Rank of International Journal of Data Warehousing and Mining

About: The International Journal of Data Warehousing and Mining (IJDWM) is a peer-reviewed journal that focuses on research related to data warehousing, data mining, and their applications. Published by IGI Global, the journal provides a platform for innovative research on methods, technologies, and applications in the field of data management and analysis.
Objective:
The objective of IJDWM is to advance the state of knowledge in data warehousing and mining by publishing high-quality research that addresses both theoretical and practical aspects of these fields. The journal aims to explore new methodologies, tools, and applications that improve the management and extraction of knowledge from large datasets.
Interdisciplinary Approach:
IJDWM takes an interdisciplinary approach by integrating insights from computer science, statistics, information systems, and business analytics. This approach enables the journal to address diverse challenges in data warehousing and mining, including data integration, pattern recognition, and decision support systems.
Impact and Significance:
The journal has a significant impact on the fields of data warehousing and mining by providing a forum for cutting-edge research and developments. IJDWM contributes to the advancement of data management technologies and methodologies, influencing both academic research and practical applications in data-driven decision-making.

Journal Home: Journal Homepage

Editor-in-Chief: Eric Pardede

Scope: The International Journal of Data Warehousing and Mining focuses on research related to data warehousing, data mining, and the integration of these technologies in handling and analyzing large datasets. The journal covers a range of topics in the field, from theoretical advances to practical applications.
Data Warehousing: Research on the design, development, and management of data warehouses, including techniques for data integration, data cleansing, and data transformation.
Data Mining Techniques: Studies on algorithms and methods for extracting useful patterns and knowledge from large datasets, including classification, clustering, association rule mining, and anomaly detection.
Big Data Analytics: Exploration of methods and technologies for analyzing and managing big data, including distributed computing frameworks, scalable storage solutions, and real-time data processing.
Data Warehousing Architectures: Examination of different architectures for data warehousing systems, including multidimensional databases, OLAP systems, and data lakes.
Business Intelligence: Research on the use of data warehousing and mining techniques in business intelligence applications, including dashboards, reporting, and decision support systems.
Data Integration and ETL Processes: Studies on techniques and tools for integrating data from multiple sources, including extract, transform, load (ETL) processes and data federation.
Data Quality and Governance: Exploration of issues related to data quality, data governance, and data stewardship, including methods for ensuring data accuracy, consistency, and reliability.
Data Privacy and Security: Research on techniques for protecting sensitive data in data warehousing and mining environments, including encryption, anonymization, and access control.
Advanced Data Mining Applications: Studies on innovative applications of data mining techniques in various domains such as healthcare, finance, marketing, and social media.
Performance Optimization: Examination of methods for optimizing the performance of data warehousing and mining systems, including query optimization, indexing, and parallel processing.
Scalable Data Management: Research on scalable solutions for managing large volumes of data, including cloud-based data warehousing and mining platforms.
Visualization and Interpretation: Studies on techniques for visualizing and interpreting the results of data mining processes, including interactive data visualizations and user interfaces.