@article {411,
	title = {An efficient use of a mapping knowledge base},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {307 - 313},
	abstract = {In a wide range of commercial and industrial applications data from different sources - web, databases or documents - are integrated into databases for the purpose of querying and analysis. It is then determined for each of the information contained in the source schema, what should be the corresponding column in the target relational database, that is to say, to establish a correspondence (Mapping) between the data source and the target column. A number of tools and prototypes of Schema Matching have been developed in order to automate the process of finding a suitable mapping. One technique employed is to use mapping decisions previously taken by the user manually. These decisions are stored in a Mapping Knowledge Base. The problem with this procedure is that the volume of the Mapping Knowledge Base increases very significantly over time. Processing large volumes of information may take several hours or even days. The prototype described in this article, keeps processing time at acceptable levels.},
	keywords = {Data integration, Relational databases, Schema matching, Similarity coefficients, XML},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84888320638\&partnerID=40\&md5=d04798bbda1e485e62f2df159a46f9a2},
	author = {Kahloula, B. and Bouamrane, K.}
}
@article {240,
	title = {An approach for matching schemas of heterogeneous relational databases},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Export Date: 10 July 2014},
	month = {2010},
	pages = {260 - 269},
	abstract = {Schema matching is a basic problem in many database application domains, such as data integration. The problem of schema matching can be formulated as follows, "given two schemas, Si and Sj, find the most plausible correspondences between the elements of Si and S j, exploiting all available information, such as the schemas, instance data, and auxiliary sources" [24]. Given the rapidly increasing number of data sources to integrate and due to database heterogeneities, manually identifying schema matches is a tedious, time consuming, error-prone, and therefore expensive process. As systems become able to handle more complex databases and applications, their schemas become large, further increasing the number of matches to be performed. Thus, automating this process, which attempts to achieve faster and less labor-intensive, has been one of the main tasks in data integration. However, it is not possible to determine fully automatically the different correspondences between schemas, primarily because of the differing and often not explicated or documented semantics of the schemas. Several solutions in solving the issues of schema matching have been proposed. Nevertheless, these solutions are still limited, as they do not explore most of the available information related to schemas and thus affect the result of integration. This paper presents an approach for matching schemas of heterogeneous relational databases that utilizes most of the information related to schemas, which indirectly explores the implicit semantics of the schemas, that further improves the results of the integration.},
	keywords = {Biomedical database, Database integration, Heterogeneous, Schema matching},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960671688\&partnerID=40\&md5=6ff8c1cb0997d2a66db189af36cbfb1c},
	author = {Karasneh, Y. and Ibrahim, H. and Othman, M. and Yaakob, R.}
}
