원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
vol.3 no.2
2010.06
pp.31-46
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
There are considerable challenges in analysing and reporting on word-based data. Infobright data warehousing technology was used to build knowledge around qualitative data that are subject to human interpretation. Infobright was chosen as a system for implementing the data set because its rough set based intelligence appears to be extensible with moderate effort to implement the data warehousing requirements for automatic interpretation of word based data. An example of social sciences research data was used for illustration.
목차
Abstract
1. Introduction
2. Infobright
3. Social Sciences Research Data: Qualitative and Quantitative
3.1. Word-based social sciences research data: A survey of those who self-injure
3.2. Software for analysis of social sciences word-based research data
3.3. Previous semantic similarity comparison metrics
3.4. Why Infobright?
4. Infobright implementation
4.1. Database design
4.2. Database definition and loading
4.3. Database query
5. Streamlining the application with existing Infobright methodology
5.1. Native support for semantic equivalence
5.2. KDD approach to computing semantic similarity
5.3. Future work
6. Summary and conclusions
7. References
1. Introduction
2. Infobright
3. Social Sciences Research Data: Qualitative and Quantitative
3.1. Word-based social sciences research data: A survey of those who self-injure
3.2. Software for analysis of social sciences word-based research data
3.3. Previous semantic similarity comparison metrics
3.4. Why Infobright?
4. Infobright implementation
4.1. Database design
4.2. Database definition and loading
4.3. Database query
5. Streamlining the application with existing Infobright methodology
5.1. Native support for semantic equivalence
5.2. KDD approach to computing semantic similarity
5.3. Future work
6. Summary and conclusions
7. References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보