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IEEE 2016 JAVA PROJECTS

 
 
Knowledge and Data Engineering:
DEFINITION
»
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.
 
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DKE reaches a world-wide audience of researchers, designers, managers and users.
 
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The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.
 
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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.
 
 
   
 
Features of DKE :
Representation and Manipulation of Data & Knowledge:
 
Conceptual data models. Knowledge representation techniques. Data/knowledge manipulation languages and techniques.
   
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.
   
Construction of data/knowledge bases:
 
Data / knowledge base design methodologies and tools, data/knowledge acquisition methods, integrity/security/maintenance issues.
   
Applications, case studies, and management issues:
 
Data administration issues, knowledge engineering practice, office and engineering applications.
   
Tools:
 
for specifying and developing Data and Knowledge Bases using tools based on Linguistics or Human Machine Interface principles.
Communication aspects :
 
involved in implementing, designing and using KBSs in Cyberspace.
 
 
 
 
 
 
 
 
 
 
 
   
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