Conjoint data mining of structured and semi-structured data
With the knowledge management requirement growing, enterprises are becoming increasingly aware of the significance of interlinking business information across structured and semi-structured data sources. This problem has become more important with the growing amount of semi-structured data often fou...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE) Computer Society
2008
|
| Online Access: | http://hdl.handle.net/20.500.11937/11398 |
| _version_ | 1848747794750242816 |
|---|---|
| author | Pan, Qi Hadzic, Fedja Dillon, Tharam S. |
| author2 | Hai Zhuge |
| author_facet | Hai Zhuge Pan, Qi Hadzic, Fedja Dillon, Tharam S. |
| author_sort | Pan, Qi |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | With the knowledge management requirement growing, enterprises are becoming increasingly aware of the significance of interlinking business information across structured and semi-structured data sources. This problem has become more important with the growing amount of semi-structured data often found in XML repositories, web logs, biological databases, etc. Effectively creating links between semi-structured and structured data is a challenging and unresolved problem. Once an optimized method has been formulated, the process of data mining can be implemented in a conjoint manner. This paper investigates a way in which this challenging problem can be tackled. The proposed method is experimentally evaluated using a real world database and the effectiveness and the potential in discovering collective information is demonstrated. |
| first_indexed | 2025-11-14T06:54:49Z |
| format | Conference Paper |
| id | curtin-20.500.11937-11398 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:54:49Z |
| publishDate | 2008 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) Computer Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-113982017-09-13T16:07:07Z Conjoint data mining of structured and semi-structured data Pan, Qi Hadzic, Fedja Dillon, Tharam S. Hai Zhuge With the knowledge management requirement growing, enterprises are becoming increasingly aware of the significance of interlinking business information across structured and semi-structured data sources. This problem has become more important with the growing amount of semi-structured data often found in XML repositories, web logs, biological databases, etc. Effectively creating links between semi-structured and structured data is a challenging and unresolved problem. Once an optimized method has been formulated, the process of data mining can be implemented in a conjoint manner. This paper investigates a way in which this challenging problem can be tackled. The proposed method is experimentally evaluated using a real world database and the effectiveness and the potential in discovering collective information is demonstrated. 2008 Conference Paper http://hdl.handle.net/20.500.11937/11398 10.1109/SKG.2008.57 Institute of Electrical and Electronics Engineers (IEEE) Computer Society fulltext |
| spellingShingle | Pan, Qi Hadzic, Fedja Dillon, Tharam S. Conjoint data mining of structured and semi-structured data |
| title | Conjoint data mining of structured and semi-structured data |
| title_full | Conjoint data mining of structured and semi-structured data |
| title_fullStr | Conjoint data mining of structured and semi-structured data |
| title_full_unstemmed | Conjoint data mining of structured and semi-structured data |
| title_short | Conjoint data mining of structured and semi-structured data |
| title_sort | conjoint data mining of structured and semi-structured data |
| url | http://hdl.handle.net/20.500.11937/11398 |