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International Journal of Business Intelligence and Data Mining - Inderscience | 2024 Cite Score:1.2 | Q4

International Journal of Business Intelligence and Data Mining With Cite Score

Cite Score and Journal Rank of International Journal of Business Intelligence and Data Mining

  • About: International Journal of Business Intelligence and Data Mining is a peer-reviewed academic journal that focuses on the application of data mining and business intelligence techniques to solve real-world business problems. The journal covers a wide range of topics related to the use of data-driven methods and technologies for decision-making and strategic planning in business contexts.
  • Objective: The primary objective of the International Journal of Business Intelligence and Data Mining is to advance the field of business intelligence and data mining by publishing high-quality research that explores new techniques, methodologies, and applications. The journal aims to provide a platform for researchers and practitioners to share innovative solutions that enhance data analysis, interpretation, and decision-making processes in business.
  • Interdisciplinary Approach: The journal adopts an interdisciplinary approach by integrating insights from various fields, including computer science, statistics, economics, management, and information systems. This approach allows for a comprehensive exploration of how data mining and business intelligence can be applied across different industries and business functions. Contributions that bridge multiple disciplines to address complex business problems are particularly encouraged.
  • Impact and Significance: The International Journal of Business Intelligence and Data Mining is significant in the field of business analytics and data science for its focus on both theoretical and practical aspects of data mining and business intelligence. The research published in the journal contributes to the development of new tools and techniques that can improve business decision-making, optimize operations, and drive competitive advantage. It serves as a valuable resource for academics, practitioners, and industry professionals interested in leveraging data for strategic and operational insights.

  • Editor-in-Chief:  Dr. M.A. Dorgham

  • Scope: The International Journal of Business Intelligence and Data Mining (IJBIDM) focuses on the intersection of business intelligence (BI) and data mining. It covers a range of topics related to the application of data analysis and mining techniques to business decision-making. The journals scope includes:
  • 1. Business Intelligence Systems: Research on the design, implementation, and evaluation of business intelligence systems, including data warehousing, data integration, and reporting tools.
  • 2. Data Mining Techniques: Exploration of various data mining methods and algorithms, including clustering, classification, association rule mining, and anomaly detection.
  • 3. Data Warehousing: Studies on data warehousing architectures, data modeling, ETL (Extract, Transform, Load) processes, and data storage solutions.
  • 4. Predictive Analytics: Research on predictive modeling techniques, including statistical methods, machine learning algorithms, and forecasting methods for business applications.
  • 5. Big Data Analytics: Exploration of techniques and technologies for analyzing large volumes of data, including distributed computing frameworks and real-time data processing.
  • 6. Data Visualization: Studies on methods and tools for visualizing complex data sets, including interactive dashboards, visual analytics, and graphical representations.
  • 7. Business Data Management: Research on data management practices, including data quality, data governance, metadata management, and data stewardship.
  • 8. Decision Support Systems: Exploration of systems and technologies that support decision-making processes, including expert systems, decision models, and simulation tools.
  • 9. Knowledge Discovery: Research on techniques for discovering actionable insights from data, including pattern recognition, feature extraction, and knowledge extraction.
  • 10. Applications in Business: Studies on the application of BI and data mining techniques in various business domains, such as finance, marketing, supply chain management, and healthcare.
  • 11. Data Mining in E-Commerce: Exploration of data mining applications in e-commerce, including customer behavior analysis, recommendation systems, and market basket analysis.
  • 12. Privacy and Security: Research on issues related to the privacy and security of data used in business intelligence and data mining, including data anonymization and encryption.
  • 13. Case Studies and Best Practices: Documentation of real-world case studies, best practices, and lessons learned from the implementation of BI and data mining solutions in organizations.
  • 14. Emerging Trends: Exploration of new and emerging trends in business intelligence and data mining, including advancements in technology and novel methodologies.
  • Latest Research Topics for PhD in Computer Science
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  • Print ISSN:  1743-8187

    Electronic ISSN:  1743-8195

  • Abstracting and Indexing:  Scopus

  • Imapct Factor :  

  • Subject Area and Category:  Business, Management and Accounting,Management Information Systems,Decision Sciences,Information Systems and Management

  • Publication Frequency:  

  • H Index:  24

  • Best Quartile:

    Q1:  

    Q2:  

    Q3:  

    Q4:  Information Systems and Management

  • Cite Score:  1.2

  • SNIP:  0.235

  • Journal Rank(SJR):  0.150