VLDB Journal

Aims & Scope

The journal is dedicated to the publication of scholarly contributions in areas of data management such as database system technology and information systems, including their architectures and applications.

Further, the journal’s scope is restricted to areas of data management that are covered by the combined expertise of the journal’s editorial board.

Submissions with a substantial theory component are welcome, but the VLDB Journal expects such submissions also to embody a systems component.

In relation to data mining, the journal will handle submissions where systems issues play a significant role.

Factors that we use to determine whether a data mining paper is within scope include: The submission targets systems issues in relation to data mining, e.g., by covering integration with a database engine or with other data management functionality.

The submission’s contributions build on (rather than simply cite) work already published in database outlets, e.g., VLDBJ, ACM TODS, PVLDB, ACM SIGMOD, IEEE ICDE, EDBT.

The journal's editorial board has the necessary expertise on the submission's topic.

Traditional, stand-alone data mining papers that lack the above or similar characteristics are out of scope for this journal.

Criteria similar to the above are applied to submission from other areas, e.g., information retrieval and geographical information systems.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.8
2024	2.80

SJR (SCImago Journal Rank)

Year	Value
2024	1.176

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	104

Journal Rank

Year	Value
2024	3680

Journal Citation Indicator

Year	Value
2024	876

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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 of approaches to automatic schema matching

  Citation: 2030

  Authors: Erhard, Philip A.

• Aurora: a new model and architecture for data stream management

  Citation: 1027

  Authors: Daniel J., Don, Ugur, Mitch, Christian, Sangdon, Michael, Nesime, Stan

• Distance-based outliers: algorithms and applications

  Citation: 877

  Authors: Edwin M., Raymond T., Vladimir

• Answering queries using views: A survey

  Citation: 864

  Authors: Alon Y.

• Service oriented architectures: approaches, technologies and research issues

  Citation: 761

  Authors: Mike P., Willem-Jan

• The CQL continuous query language: semantic foundations and query execution

  Citation: 753

  Authors: Arvind, Shivnath, Jennifer

• The RDF-3X engine for scalable management of RDF data

  Citation: 432

  Authors: Thomas, Gerhard

• Composing Web services on the Semantic Web

  Citation: 382

  Authors: Brahim, Athman, Ahmed K.

• Efficient query evaluation on probabilistic databases

  Citation: 357

  Authors: Nilesh, Dan

• Data collection and quality challenges in deep learning: a data-centric AI perspective

  Citation: 341

  Authors: Steven Euijong, Yuji, Hwanjun, Jae-Gil

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