Evaluating Musculoskeletal Complications Among Individuals With Diabetes Mellitus

Dr. Simran Maheshwari; Dr. Subhan Quraishi; Dr. Babita Ghodke

Volume : II, Issue : IV, April - 2013

Evaluating Musculoskeletal Complications Among Individuals With Diabetes Mellitus

Dr. Simran Maheshwari, Dr. Subhan Quraishi, Dr. Babita Ghodke

Abstract :

The schema–matching problem at the most basic level refers to the problem of mapping schema elements (for example, columns in a relational database schema) in one information repository to corresponding elements in a second repository. While schema matching has always been a problematic and interesting aspect of information integration, the problem is exacerbated as the number of information sources to be integrated, and hence, the number of integration problems that must be solved, grows. Such schema–matching problems arise both in �classical� scenarios such as company mergers and in �new� scenarios such as the integration of diverse sets of queriable information sources over the web. Purely manual solutions to the schema–matching problem are too labor intensive to be scalable; as a result, there has been a great deal of research into automated techniques that can speed this process by either automatically discovering good mappings, or by proposing likely matches that are then verified by some human expert .In this paper, we present such an automated technique that is designed. Assistance in the particularly difficult cases in which the column names and data values are �opaque,� and/or cases in which the column names are opaque and the data values in multiple columns are drawn from the same domain. Our approach works by computing the �mutual information� between pairs of columns within each schema, and then using this statistical characterization of pairs of columns in one schema to propose matching pairs of columns in the other schema