A Schema Matching Method Based on Partial Functional Dependencies

2008 ◽  
Author(s):  
Li Guo-Hui ◽  
Du Xiao-Kun ◽  
Hu Fang-Xiao ◽  
Du Jian-Qiang
Keyword(s):  
Schema Matching ◽  
Functional Dependencies ◽  
Matching Method

A Structure Matching Method Based on Partial Functional Dependencies

2010 ◽  
Vol 33 (2) ◽  
pp. 240-250 ◽  
Author(s):  
Guo-Hui LI ◽  
Xiao-Kun DU ◽  
Jian-Qiang DU
Keyword(s):  
Functional Dependencies ◽  
Matching Method

A LEXICAL DECISION TREE SCHEME FOR SUPPORTING SCHEMA MATCHING

2011 ◽  
Vol 10 (03) ◽  
pp. 519-537 ◽  
Author(s):  
BEEN-CHIAN CHIEN ◽  
SHIANG-YI HE
Keyword(s):  
Decision Tree ◽  
Lexical Decision ◽  
Structural Similarity ◽  
Schema Matching ◽  
Similarity Matching ◽  
Matching Method ◽  
Matching Algorithm ◽  
Essential Step ◽  
Structure Similarity ◽  
Two Phases

To manipulate semantic web and integrate different data sources efficiently, automatic schema matching plays a key role. A generic schema matching method generally includes two phases: the linguistic similarity matching phase and the structural similarity matching phase. Since linguistic matching is an essential step for effective schema matching, developing a high accurate linguistic similarity matching scheme is required. In this paper, a schema matching approach called Similarity Yield Matcher (SYM) is proposed. In SYM, a lexical decision tree is presented to determine the linguistic similarity matching of the first phase. A structural matching algorithm is then proposed to find the structure similarity between two tree schemas. The proposed schema matching approach was evaluated by testing on several benchmarks of real schemas and comparing with other methods. The experimental results show that the proposed lexical decision tree substantially improves the linguistic similarity matching effectively and efficiently. The proposed SYM algorithm also performs high effectiveness on 1–1 schema matching.

scholarly journals Optimization of data integration using schema matching of linguistic-based and constraint-based in the university database

2021 ◽  
Vol 11 (3) ◽  
pp. 119-129
Author(s):  
Rifqi Hammad ◽  
◽  
Azriel Christian Nurcahyo ◽  
Ahmad Zuli Amrullah ◽  
Pahrul Irfan ◽  
...  
Keyword(s):  
Information System ◽  
Information Systems ◽  
Data Integration ◽  
Data Entry ◽  
Optimization Process ◽  
Schema Matching ◽  
Matching Method ◽  
Integration Problems ◽  
The University

University requires the integration of data from one system with other systems as needed. This is because there are still many processes to input the same data but with different information systems. The application of data integration generally has several obstacles, one of which is due to the diversity of databases used by each information system. Schema matching is one method that can be used to overcome data integration problems caused by database diversity. The schema matching method used in this research is linguistic and constraint. The results of the matching scheme are used as material for optimizing data integration at the database level. The optimization process shows a change in the number of tables and attributes in the database that is a decrease in the number of tables by 13 tables and 492 attributes. The changes were caused by some tables and attributes were omitted and normalized. This research shows that after optimization, data integration becomes better because the data was connected and used by other systems has increased by 46.67% from the previous amount. This causes the same data entry on different systems can be reduced and also data inconsistencies caused by duplication of data on different systems can be minimized.

scholarly journals Optimization of data integration using schema matching of linguistic-based and constraint-based in the university database

2021 ◽  
Vol 11 (3) ◽  
pp. 119-129
Author(s):  
Rifqi Hammad ◽  
◽  
Azriel Christian Nurcahyo ◽  
Ahmad Zuli Amrullah ◽  
Pahrul Irfan ◽  
...  
Keyword(s):  
Information System ◽  
Information Systems ◽  
Data Integration ◽  
Data Entry ◽  
Optimization Process ◽  
Schema Matching ◽  
Matching Method ◽  
Integration Problems ◽  
The University

University requires the integration of data from one system with other systems as needed. This is because there are still many processes to input the same data but with different information systems. The application of data integration generally has several obstacles, one of which is due to the diversity of databases used by each information system. Schema matching is one method that can be used to overcome data integration problems caused by database diversity. The schema matching method used in this research is linguistic and constraint. The results of the matching scheme are used as material for optimizing data integration at the database level. The optimization process shows a change in the number of tables and attributes in the database that is a decrease in the number of tables by 13 tables and 492 attributes. The changes were caused by some tables and attributes were omitted and normalized. This research shows that after optimization, data integration becomes better because the data was connected and used by other systems has increased by 46.67% from the previous amount. This causes the same data entry on different systems can be reduced and also data inconsistencies caused by duplication of data on different systems can be minimized.

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