Impact Factor and Journal Rank of Data & Knowledge Engineering

About:

Data & Knowledge Engineering (DKE) is a peer-reviewed journal that publishes research in the field of data and knowledge engineering. The journal focuses on the development and application of methodologies and technologies related to data engineering and knowledge engineering, providing a platform for the dissemination of significant research findings and advancements in these areas.

Impact and Significance

Data & Knowledge Engineering is significant for several reasons:

Advancing the Field: It contributes to the advancement of data and knowledge engineering by publishing high-quality, peer-reviewed research.
Technological Innovations: The journal highlights innovative methodologies and technologies that address current challenges in data and knowledge management.
Interdisciplinary Research: It promotes interdisciplinary research, encouraging collaboration between computer scientists, engineers, and practitioners.
Practical Applications: The journal facilitates the application of research findings to real-world problems in various industries, including healthcare, finance, and e-commerce.
Global Reach: It serves as a platform for researchers from around the world to share their insights and discoveries.

Types of Articles Accepted

Data & Knowledge Engineering accepts a variety of submission types, including:

Research Papers: Detailed studies presenting original research findings in data and knowledge engineering.
Survey Articles: Comprehensive reviews of the current state of research in specific areas within the field.
Application Papers: Papers demonstrating the application of data and knowledge engineering techniques to solve practical problems.
Technical Notes: Brief reports on novel methods, tools, or technologies.
Case Studies: Practical implementations and case studies highlighting the use of data and knowledge engineering methods in real-world scenarios.

Journal Home:  Journal Homepage

Editor-in-Chief:  Carson Woo

scope: Data & Knowledge Engineering is a peer-reviewed journal that publishes high-quality research articles, reviews, and theoretical papers on data engineering, data management, and knowledge engineering. Scope: The journal covers a broad range of topics within data engineering and knowledge management, including but not limited to:

Data Modeling and Management: Research on data modeling techniques, database design, data storage, and data retrieval methods.

Knowledge Representation and Reasoning: Studies on the representation of knowledge, reasoning techniques, and knowledge-based systems.

Data Integration and Interoperability: Research on methods for integrating data from diverse sources and ensuring interoperability among different data systems.

Big Data and Data Analytics: Studies on the processing, analysis, and management of large-scale data sets, including big data technologies and analytics.

Data Mining and Machine Learning: Research on data mining techniques, machine learning algorithms, and their applications in extracting knowledge from data.

Information Retrieval and Text Mining: Studies on information retrieval techniques, text mining methods, and their applications in knowledge discovery.

Semantic Web and Linked Data: Research on the Semantic Web, linked data, and technologies for enhancing data and knowledge sharing on the web.

Ontologies and Conceptual Modeling: Studies on the development and application of ontologies, conceptual models, and frameworks for knowledge representation.

Data Quality and Data Governance: Research on data quality assessment, data governance frameworks, and methodologies for ensuring data integrity and reliability.

Data Privacy and Security: Studies on methods for ensuring data privacy, data protection, and security in data management systems.

Applications and Case Studies: Practical applications of data engineering and knowledge management techniques in various domains, such as healthcare, finance, e-commerce, and social media.

Print ISSN:  0169-023X

Electronic ISSN:  

Abstracting and Indexing:  Science Citation Index Expanded, Scopus.

Imapct Factor 2023:  2.7

Subject Area and Category:  Computer Sciences, Civil Engineering, Library and Information Science

Publication Frequency:  Bimonthly

H Index:  94

Best Quartile:

Q1:  

Q2:  Information Systems and Management

Q3:  

Q4:  

Cite Score:  5.0

SNIP:  1.448

Journal Rank(SJR):  0.691

Latest Articles:   Latest Articles in Data & Knowledge Engineering

Guidelines for Authors: Data & Knowledge Engineering Author Guidelines

Paper Submissions: Paper Submissions in Data & Knowledge Engineering

Publisher:  Elsevier Science B.V

Country:  Netherlands