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IEEE Transactions on Knowledge and Data Engineering | 2024 Impact Factor 10.4| Cite Score:15.7 | Q1

IEEE Transactions on Knowledge and Data Engineering Journal

Impact Factor and Journal Rank of IEEE Transactions on Knowledge and Data Engineering

  • About: The IEEE Transactions on Knowledge and Data Engineering (TKDE) is a prestigious, peer-reviewed journal dedicated to research in the areas of knowledge and data engineering. Published by the IEEE Computer Society, TKDE serves as a leading platform for the dissemination of high-quality research findings and innovations in the field. It covers a wide range of topics related to the management, processing, and analysis of data and knowledge, with an emphasis on theoretical foundations, practical applications, and emerging trends in data-driven technologies.
  • Content: Research Articles: Original research papers presenting novel contributions, theoretical advancements, and empirical findings in knowledge and data engineering. Comprehensive surveys and systematic literature reviews that summarize and analyze the state-of-the-art in specific areas of knowledge and data engineering, identifying key trends and research challenges.
  • High Standards and Impact: The journal maintains rigorous standards of quality and integrity through a double-blind peer-review process, ensuring that published articles meet high scientific and technical standards. It is recognized for its impact and influence in the knowledge and data engineering community, with articles frequently cited by researchers, practitioners, and educators worldwide.
  • Global Reach: The "IEEE Transactions on Knowledge and Data Engineering" attracts contributions from researchers and practitioners across the globe, fostering a diverse and inclusive community of knowledge and data engineering experts.
  • Significance: It is a premier academic journal that significantly contributes to the advancement of knowledge and practice in the field of knowledge and data engineering. Its broad scope, high standards, and global reach make it an indispensable resource for researchers, practitioners, educators, and policy makers interested in the theory, methodologies, and applications of knowledge and data engineering in todays data-driven world.

  • Editor-in-Chief:  Lei Chen

  • Scope: The IEEE Transactions on Knowledge and Data Engineering (TKDE) is a leading journal that publishes high-quality, peer-reviewed articles covering a wide range of topics related to knowledge and data engineering.
  • The scope of the journal encompasses both theoretical and practical research in various areas, including but not limited to:
  • Data Mining and Knowledge Discovery: Research on algorithms, techniques, and methodologies for discovering useful patterns, knowledge, and insights from large datasets, including association rule mining, clustering, classification, and anomaly detection.
  • Machine Learning for Data Mining: Advances in machine learning techniques and algorithms applied to data mining tasks, including supervised, unsupervised, semi-supervised, and reinforcement learning approaches.
  • Big Data Analytics: Methods and tools for processing, analyzing, and extracting valuable information from big data, including distributed and parallel processing, scalable algorithms, and cloud computing platforms.
  • Data Warehousing and OLAP: Design, implementation, and optimization of data warehouses and online analytical processing (OLAP) systems for efficient storage, retrieval, and analysis of multidimensional data.
  • Data Integration and Fusion: Techniques for integrating and fusing data from heterogeneous sources, including data cleaning, schema matching, data transformation, and data interoperability.
  • Knowledge Representation and Reasoning: Formalisms, languages, and algorithms for representing and reasoning about knowledge, including ontologies, knowledge graphs, semantic web technologies, and automated reasoning systems.
  • Knowledge Discovery in Databases (KDD): Studies on the process of knowledge discovery in databases, including data preprocessing, feature selection, pattern mining, and evaluation of discovered knowledge.
  • Semantic Web and Linked Data: Research on the Semantic Web technologies, linked data principles, and ontological engineering for organizing, sharing, and reasoning about data and knowledge on the web.
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  • Print ISSN:  1041-4347

    Electronic ISSN:  1558-2191

  • Abstracting and Indexing:  Science Citation Index Expanded, Scopus.

  • Imapct Factor 2024:  10.4

  • Subject Area and Category:  Computer Sciences, Library and Information Science, Mathematics.

  • Publication Frequency:  Monthly

  • H Index:  216

  • Best Quartile:

    Q1:  Computational Theory and Mathematics

    Q2:  

    Q3:  

    Q4:  

  • Cite Score:  15.7

  • SNIP:  3.304

  • Journal Rank(SJR):  2.570