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Schema matching for Database Systems Bhavik Doshi Chair: Prof. Rajendra K. Raj Reader: Dr. Carol Romanowski Department of Computer Science Rochester Institute.

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Presentation on theme: "Schema matching for Database Systems Bhavik Doshi Chair: Prof. Rajendra K. Raj Reader: Dr. Carol Romanowski Department of Computer Science Rochester Institute."— Presentation transcript:

1 Schema matching for Database Systems Bhavik Doshi Chair: Prof. Rajendra K. Raj Reader: Dr. Carol Romanowski Department of Computer Science Rochester Institute of Technology 01/13/2009

2 Schema Matching Given: Source and Target schemas Matching: Maps the source schema elements to target schema elements. Employee Salary EMPID SSN Name Position EmployeePosition-type has Salary Position SSN NameEMPID 01/13/2009

3 Why Schema Matching? First step of Data Integration Upcoming field in Data management research because of its important role in Enterprise Information Integration. Building data warehouses and marts Manual approach is very laborious 01/13/2009

4 Motivation 01/13/2009

5 Syntactical Approach Uses Syntax used for naming databases Elemental Level Approach IDNAMESSN EMP_ID EMP_NAME EMP_SSN ? Random Names 01/13/2009

6 Data value and Constraint based approach Uses data values, data types, comparison of ranges. Instance based approach KeyAddPh 001 Sector 1 7783453 002 Sector 2 4563216 005 Sector 1 7783453 E_ID E_AE_P 1 Some address5857339400 3 Random address5856234532 5 Some address5856778345 ? Data values not appropriate 01/13/2009

7 Hypothesis & Objective 01/13/2009

8 Hypothesis Relying on a single technique for schema matching may not always succeed. Each approach is implemented independently of the others and so the overall impact is not as effective. Develop an integrated technique which is domain independent for oracle databases. 01/13/2009

9 Objective Develop a generic integrated matching technique. Implement two substantially different techniques – Kang et al. – Lingmei et al. Transform steps of the above two algorithms and make use of additional techniques for better matching. Testing the developed technique with (~30) relational datasets and then observing the results. 01/13/2009

10 Algorithms Instance Based schema matching – Kang et al. Element Level schema matching – Lingmei et al. 01/13/2009

11 Integrated Approach Kang’s Approach Lingmei’s Approach Probability Distribution Structure Syntax Semantics Data Type and Range Instance BasedElement Based Route to Success!! 01/13/2009

12 Questions?? 01/13/2009


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