1. Data mining, which is a shortened name for the original subject area of data mining and knowledge discovery.
2. Databases and data modeling, which consolidates the original subject areas of databases, data structures and algorithms, distributed databases, and object-oriented systems.
3. Knowledge engineering and intelligent systems, which covers the original subject areas of artificial intelligence, knowledge management, knowledge processing, and languages and interfaces.
4. Underlying computational platforms for knowledge and data engineering, which is basically a new subject area, and provides itemized topics that are relevant to the above three focus areas.
5. Emerging knowledge and data engineering applications, which is an existing subject area with more application domains.
• It is unclear or too general and needs to be renamed.
• It is too specialized or otherwise to be listed a research topic.
• It has been subsumed by other topics.
• There is no recent interest.
1. In data mining: outlier detection and spatial and temporal data mining.
2. In databases and data modeling: caching and prefetching, data quality, mobile databases, and schema evolution.
3. In knowledge engineering and intelligent systems: case-based reasoning, evolutionary computing, information extraction, ontologies, soft computing, and semantic Web.
4. In underlying computational platforms for knowledge and data engineering: agent architectures and systems, hybrid computing, and mobile systems.
5. In emerging knowledge and data engineering applications: bioinformatics, business intelligence, digital libraries, health science and medical systems, privacy, security, social networks and graph analysis, streams and sensor databases, and Web services.
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