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
On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is e...On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is essential for on-site programming big data.Duplicate data detection is an important step in data cleaning,which can save storage resources and enhance data consistency.Due to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed.The efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding window.Experimental results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.展开更多
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.
基金supported by the National Key R&D Program of China(Nos.2018YFB1003905)the National Natural Science Foundation of China under Grant No.61971032,Fundamental Research Funds for the Central Universities(No.FRF-TP-18-008A3).
文摘On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is essential for on-site programming big data.Duplicate data detection is an important step in data cleaning,which can save storage resources and enhance data consistency.Due to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed.The efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding window.Experimental results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.
