Most semi-structured data are of certain structure regularity. Having beenstored as structured data in relational database (RDB), they can be effectively managed by databasemanagement system (DBMS). Some semi-structur...Most semi-structured data are of certain structure regularity. Having beenstored as structured data in relational database (RDB), they can be effectively managed by databasemanagement system (DBMS). Some semi-structured data are difficult to transform due to theirirregular structures. We design an efficient algorithm and data structure for ensuring losslesstransformation. We bring forward an approach of schema extraction through data mining, in whichdifferent kinds of elements are transformed respectively and lossless mapping from semi-structureddata to structured data can be achieved.展开更多
A duplicate identification model is presented to deal with semi-structured or unstructured data extracted from multiple data sources in the deep web.First,the extracted data is generated to the entity records in the d...A duplicate identification model is presented to deal with semi-structured or unstructured data extracted from multiple data sources in the deep web.First,the extracted data is generated to the entity records in the data preprocessing module,and then,in the heterogeneous records processing module it calculates the similarity degree of the entity records to obtain the duplicate records based on the weights calculated in the homogeneous records processing module.Unlike traditional methods,the proposed approach is implemented without schema matching in advance.And multiple estimators with selective algorithms are adopted to reach a better matching efficiency.The experimental results show that the duplicate identification model is feasible and efficient.展开更多
文摘Most semi-structured data are of certain structure regularity. Having beenstored as structured data in relational database (RDB), they can be effectively managed by databasemanagement system (DBMS). Some semi-structured data are difficult to transform due to theirirregular structures. We design an efficient algorithm and data structure for ensuring losslesstransformation. We bring forward an approach of schema extraction through data mining, in whichdifferent kinds of elements are transformed respectively and lossless mapping from semi-structureddata to structured data can be achieved.
基金The National Natural Science Foundation of China(No.60673139)
文摘A duplicate identification model is presented to deal with semi-structured or unstructured data extracted from multiple data sources in the deep web.First,the extracted data is generated to the entity records in the data preprocessing module,and then,in the heterogeneous records processing module it calculates the similarity degree of the entity records to obtain the duplicate records based on the weights calculated in the homogeneous records processing module.Unlike traditional methods,the proposed approach is implemented without schema matching in advance.And multiple estimators with selective algorithms are adopted to reach a better matching efficiency.The experimental results show that the duplicate identification model is feasible and efficient.
