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 ... click here for full 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.
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.
Hide Aims & Scope
Editor:
P.P. Chen (Editor-in-Chief)
Please join Elsevier and
Data and Knowledge Engineering in congratulating Professor Bernhard Thalheim, recipient of the Peter Chen Award,
AND
Lipyeow Lim, Haixun Wang, and Min Wang, recipients of the Best Paper Award, for their article entitled "Modeling and Querying E-Commerce Data in Hybrid Relational-XML DBMSs."
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