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.Benefits to authors We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our Support Center
Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, we prefer papers that discuss development of new AI architectures, methodologies, and techniques and their applications to the field of data analysis. Papers published in this journal are geared heavily towards applications, with an anticipated split of 70% of the papers published being applications-oriented, and the remaining 30% containing more theoretical material.Formerly published as an electronic journal - for 1999 will be published as a paper journal with electronic access.
