IEEE Transactions on Knowledge and Data Engineering

Aims & Scope

The scope includes the knowledge and data engineering aspects of computer science, artificial intelligence, electrical engineering, computer engineering, and other appropriate fields.

This Transactions provides an international and interdisciplinary forum to communicate results of new developments in knowledge and data engineering and the feasibility studies of these ideas in hardware and software.

Specific areas to be covered are as follows: Fields and Areas of Knowledge and Data Engineering: (a) Knowledge and data engineering aspects of knowledge based and expert systems, (b) Artificial Intelligence techniques relating to knowledge and data management, (c) Knowledge and data engineering tools and techniques, (d) Distributed knowledge base and database processing, (e) Real-time knowledge bases and databases, (f) Architectures for knowledge and data based systems, (g) Data management methodologies, (h) Database design and modeling, (i) Query, design, and implementation languages, (j) Integrity, security, and fault tolerance, (k) Distributed database control, (l) Statistical databases, (m) System integration and modeling of these systems, (n) Algorithms for these systems, (o) Performance evaluation of these algorithms, (p) Data communications aspects of these systems, and (q) Applications of these systems.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	10.4

Journal Rank

Year	Value
2024	917

Journal Citation Indicator

Year	Value
2024	18208

SJR (SCImago Journal Rank)

Year	Value
2024	2.570

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	216

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A Survey on Transfer Learning

  Citation: 17297

  Authors: Sinno Jialin, Qiang

• Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

  Citation: 7061

  Authors: G., A.

• Learning from Imbalanced Data

  Citation: 6814

  Authors: E.A.

• A Review on Multi-Label Learning Algorithms

  Citation: 2430

  Authors: Min-Ling, Zhi-Hua

• Knowledge Graph Embedding: A Survey of Approaches and Applications

  Citation: 1849

  Authors: Quan, Zhendong, Bin, Li

• Toward integrating feature selection algorithms for classification and clustering

  Citation: 1817

  Authors:

• Data mining with big data

  Citation: 1733

  Authors:

• Using AUC and accuracy in evaluating learning algorithms

  Citation: 1705

  Authors: C.X.

• Protecting respondents identities in microdata release

  Citation: 1609

  Authors: P.

• Workflow mining: discovering process models from event logs

  Citation: 1528

  Authors: W., T., L.

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