Why Researchers Choose Data & Knowledge Engineering for High-Impact Publications
Data & Knowledge Engineering has established itself as a cornerstone in the field of computer science, particularly for those focused on database administration and knowledge systems. Launched in 1985 by Elsevier, this journal bridges theoretical foundations with practical applications in data handling, knowledge representation, and engineering methodologies. Researchers are drawn to its commitment to innovation, where papers explore everything from advanced database architectures to semantic web technologies and machine learning integrations.
The journal's rigorous peer-review process ensures that only high-quality, impactful work sees publication, making it a preferred choice for academics aiming to contribute to evolving discussions in data management. With a scope that encompasses conceptual modeling, query optimization, and knowledge discovery, Data & Knowledge Engineering provides a platform for interdisciplinary dialogue. Its international editorial board, comprising experts from leading institutions worldwide, upholds standards that resonate with the global research community.
Publishing here not only amplifies visibility through Elsevier's extensive network but also connects authors to influential citations in subsequent studies. For database administrators and computer scientists, submitting to Data & Knowledge Engineering means aligning with a publication history rich in seminal works that have shaped modern data engineering practices. The journal's hybrid model allows flexibility in open access options, enhancing dissemination without compromising accessibility.
As data volumes explode in the digital age, the relevance of Data & Knowledge Engineering continues to grow, offering insights into scalable systems and intelligent data processing. Whether addressing big data challenges or knowledge-based decision systems, contributions here drive real-world advancements. To explore opportunities in database administration roles that value such publications, consider browsing database administrator jobs for career inspiration.
Overview & History
Data & Knowledge Engineering, often abbreviated as DKE, was founded in 1985 to address the burgeoning needs of the database and knowledge engineering communities. Published by Elsevier, it emerged during a pivotal era when relational databases were gaining traction and expert systems were revolutionizing artificial intelligence. Over the decades, the journal has evolved to cover emerging paradigms like NoSQL databases, data mining, and ontology engineering.
Its history reflects the field's maturation, from early focuses on logical data models to contemporary emphases on cloud computing and data privacy. Key milestones include special issues on semantic technologies in the 2000s and recent collections on AI-driven data analytics. Today, Data & Knowledge Engineering remains a vital resource, indexed in major databases and cited by thousands annually.
Scope and Disciplines Covered
The scope of Data & Knowledge Engineering encompasses a wide array of topics at the intersection of data management and knowledge systems. It welcomes submissions on database design, knowledge acquisition, and engineering applications in various domains.
| Discipline | Description |
|---|---|
| Database Systems | Focuses on modeling, querying, and optimization of relational and non-relational databases. |
| Knowledge Engineering | Explores knowledge representation, ontologies, and expert systems development. |
| Artificial Intelligence | Covers AI integration with data engineering, including machine learning for data analysis. |
| Information Systems | Addresses data integration, security, and big data technologies. |
| Software Engineering | Examines methodologies for building knowledge-intensive software. |
These disciplines align closely with database administration roles, providing practical insights for professionals. For further reading on related academic paths, visit computer science PhD programs.
Key Journal Metrics
| Metric | Value | Source |
|---|---|---|
| Impact Factor | 2.0 | Clarivate JCR 2022 |
| CiteScore | 4.9 | Scopus 2022 |
| SJR | 0.64 | Scopus |
| h-index | 72 | Scopus |
| Acceptance Rate | Not publicly disclosed | N/A |
These metrics underscore the journal's influence in database and knowledge engineering research.
Indexing and Abstracting
Data & Knowledge Engineering is indexed in prestigious databases, ensuring broad discoverability. It appears in Scopus, Web of Science (Science Citation Index Expanded), and INSPEC. Additional coverage includes DBLP for computer science literature and Google Scholar. For open access visibility, it is listed in DOAJ-eligible categories, though as a hybrid journal, full OA indexing varies.
Researchers can access abstracts via the official site: Data & Knowledge Engineering homepage. Explore indexing details on Scopus.
Publication Model and Fees
As a hybrid journal, Data & Knowledge Engineering offers both subscription-based and open access publication options. Authors can publish traditionally with no fees, or opt for gold open access by paying an Article Processing Charge (APC) of approximately €2,530 (excluding taxes). This model supports wider dissemination while maintaining rigorous standards.
Elsevier's policies, detailed on Sherpa/RoMEO, allow self-archiving of preprints. For funding advice, check grants and funding opportunities in academia.
Submission Process and Guidelines
Submissions to Data & Knowledge Engineering are handled through Elsevier's Editorial Manager system. Authors must prepare manuscripts in LaTeX or Word, adhering to guidelines on structure, length (up to 30 pages), and ethics. Key steps include abstract submission, peer review (typically 3-6 months), and revisions.
Focus on originality and relevance to database or knowledge engineering. The portal is accessible via the journal site. For preparation tips, see academic calendar for submission deadlines.
Editorial Board Highlights
The editorial board of Data & Knowledge Engineering features distinguished scholars. Editor-in-Chief Oscar Pastor from Universitat Politècnica de València leads with expertise in conceptual modeling. Other notables include Yahya Rahmani in knowledge engineering and international members from the US, Europe, and Asia, ensuring diverse perspectives.
This board's composition reflects the journal's global reach and commitment to quality.
Why Publish in Data & Knowledge Engineering?
Publishing in Data & Knowledge Engineering offers researchers enhanced credibility and networking opportunities. Its impact factor attracts citations, boosting h-indexes crucial for tenure. The journal's focus on practical applications appeals to database administrators seeking to influence industry standards.
Moreover, Elsevier's marketing amplifies reach, connecting papers to policymakers and practitioners. For career benefits, review Rate My Professor for database faculty insights.
Comparison with Similar Journals
| Journal | Impact Factor | Publisher | Focus |
|---|---|---|---|
| VLDB Journal | 3.1 | Springer | Very Large Databases |
| Journal on Data Semantics | 1.2 | Springer | Semantic Web |
| Information Systems | 2.8 | Elsevier | IS Design |
| Knowledge-Based Systems | 8.1 | Elsevier | AI Knowledge |
Data & Knowledge Engineering holds a balanced position, emphasizing engineering aspects over pure theory.
Researcher Tips for Successful Submission
- Align your work closely with the journal's scope, emphasizing novel contributions to data or knowledge engineering.
- Conduct a thorough literature review, citing recent Data & Knowledge Engineering articles.
- Ensure clear methodology and empirical validation in your manuscript.
- Seek feedback from peers before submission to strengthen arguments.
- Monitor tenure-track positions in computer science to understand publication expectations.
These strategies increase acceptance chances. For more, explore academic advising resources.