Publication Type:

Conference Proceedings

Source:

Advances in Intelligent Systems and Computing, 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, IEEE SPICES, Springer Verlag, Volume 385, Kochi, India, p.333-344 (2016)

ISBN:

9783319232577

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84985029747&partnerID=40&md5=2b983db6fbb76192e8c49565a7845a4b

Keywords:

Data integration, Heterogeneous data sources, Heterogeneous sources, Incomplete knowledge, Intelligent systems, Knowledge base, Knowledge based systems, Q R decomposition, Recommendation strategies, Semantic conflict, Semantic integration, Semantics

Abstract:

Integration of data residing at different sites and providing users with a unified view of these data is being extensively studied for commercial and scientific purposes. Amongst various concerns of integration, semantic integration is the most challenging problem that addresses the resolution of the semantic conflicts between heterogeneous data sources. Even if the data sources may belong to similar domain, due to the lack of commonality in the schema of databases and the instances of databases, the unified result of the integration may be inaccurate and difficult to validate. So, identification of the most significant or independent attributes of each data source and then providing a unified view of these is a challenge in the realm of heterogeneity. This demands for proper analysis of each data source in order to have a comprehensive meaning and structure of the same. The contribution of this paper is in the realization of semantic integration of heterogeneous sources of similar domain using QR decomposition, together with a bridging knowledge base. The independent attributes of each data source are found that are integrated based on the similarity or correlation amongst them, for forming a global view of all the data sources, with the aid of a knowledge base. In case of an incomplete knowledge base, we also formulate a recommendation strategy for the integration of the possible set of attributes. Experimental results show the feasibility of this approach with the data sources of same domain. © Springer International Publishing Switzerland 2016.

Notes:

cited By 0; Conference of International Symposium on Intelligent Systems Technologies and Applications, ISTA 2015 co-located with 4th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015 ; Conference Date: 10 August 2015 Through 13 August 2015; Conference Code:179339

Cite this Research Publication

Sandhya Harikumar and Roy, M. M., “Data integration of heterogeneous data sources using QR decomposition”, Advances in Intelligent Systems and Computing, 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, IEEE SPICES, vol. 385. Springer Verlag, Kochi, India, pp. 333-344, 2016.