Data & Knowledge Engineering

Data & Knowledge Engineering - ISSN 0169-023X
Source Normalized Impact per Paper (SNIP): 2.412 Source Normalized Impact per Paper (SNIP):
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 1.258 SCImago Journal Rank (SJR):
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Impact Factor: 1.694 (2016) Impact Factor:
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© Thomson Reuters Journal Citation Reports 2015
5 Year Impact Factor: 2.131 (2016) Five-Year Impact Factor:
To calculate the five year Impact Factor, citations are counted in 2014 to the previous five years and divided by the source items published in the previous five years.
© Journal Citation Reports 2015, Published by Thomson Reuters
Volumes: Volumes 107-112
Issues: 6 issues
ISSN: 0169023X

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Description

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.

DKE covers the following topics:

1. Representation and Manipulation of Data & Knowledge: Conceptual data models. Knowledge representation techniques. Data/knowledge manipulation languages and techniques.

2. Architectures of database, expert, or knowledge-based systems: New architectures for database / knowledge base / expert systems, design and implementation techniques, languages and user interfaces, distributed architectures.

3. Construction of data/knowledge bases: Data / knowledge base design methodologies and tools, data/knowledge acquisition methods, integrity/security/maintenance issues.

4. Applications, case studies, and management issues: Data administration issues, knowledge engineering practice, office and engineering applications.

5. Tools for specifying and developing Data and Knowledge Bases using tools based on Linguistics or Human Machine Interface principles.

6. Communication aspects involved in implementing, designing and using KBSs in Cyberspace.

Plus... conference reports, calendar of events, book reviews etc.

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