Data and Knowledge Engineering

Aims & Scope

Database Systems and Knowledgebase Systems share many common principles.

Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest.

DKE reaches a world-wide audience of researchers, designers, managers and users.

The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.

DKE achieves this aim by publishing original research results, technical advances and news items concerning data engineering, knowledge engineering, and the interface of these two fields.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.6

SJR (SCImago Journal Rank)

Year	Value
2024	0.682

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	96

Journal Rank

Year	Value
2024	8365

Journal Citation Indicator

Year	Value
2024	705

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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 Decision Sciences, 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.

• Knowledge engineering: Principles and methods

  Citation: 2192

  Authors: Rudi, V.Richard, Dieter

• ST-DBSCAN: An algorithm for clustering spatial–temporal data

  Citation: 1079

  Authors: Derya, Alp

• Workflow mining: A survey of issues and approaches

  Citation: 725

  Authors: W.M.P., B.F., J., L., G., A.J.M.M.

• A k-mean clustering algorithm for mixed numeric and categorical data

  Citation: 539

  Authors: Amir, Lipika

• Case handling: a new paradigm for business process support

  Citation: 533

  Authors: Wil M.P., Mathias, Dolf

• A conceptual view on trajectories

  Citation: 438

  Authors: Stefano, Christine, Maria Luisa, Jose Antonio, Fabio, Christelle

• Change patterns and change support features – Enhancing flexibility in process-aware information systems

  Citation: 421

  Authors: Barbara, Manfred, Stefanie

• Methodologies, tools and languages for building ontologies. Where is their meeting point?

  Citation: 398

  Authors: Oscar, Mariano, Asunción

• IT support for healthcare processes – premises, challenges, perspectives

  Citation: 352

  Authors: Richard, Manfred

• Computing iceberg concept lattices with Titanic

  Citation: 300

  Authors: Gerd, Rafik, Yves, Nicolas, Lotfi

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