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Conjoint data mining of structured and semi-structured data

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Open access
Authors
Pan, Qi
Hadzic, Fedja
Dillon, Tharam S.
Date
2008
Type
Conference Paper

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Citation
Pan, Qi and Hadzic, Fedja and Dillon, Tharam. 2008. Conjoint data mining of structured and semi-structured data, in Zhuge, H. (ed), Fourth International Conference on Semantics, Knowledge and Grid, Dec 3 2008, pp. 87-94. Beijing, China: Institute of Electrical and Electronics Engineers (IEEE) Computer Society.
Source Title
Proceedings of the 4th international conference on semantics, knowledge and grid (SKG 2008)
Source Conference
4th International Conference on Semantics, Knowledge and Grid (SKG 2008)
DOI
ISBN
Faculty
Curtin Business School
School of Information Systems
School
Centre for Extended Enterprises and Business Intelligence
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Abstract

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.