Visual Resemblance Based Content Descent for Multiset Query Records using Novel Segmentation Algorithm

Online data request and respond to a user query with result records are programmed in HTML files. Extracting information from the unstructured bases has matured into a significant technical challenge whereas generally, data extraction had to deal with changes in physical hardware plans, the majority of current data mining deals with extracting data from the unstructured data sources, and from dissimilar software plans. In this paper, we focus on the problem of automatically extracting data records that are encoded in the query result pages generated by web data records. We propose an unusual data extraction scheme called improved combined tag and value similarity (R-SEGMENT algorithm) approach. R-SEGMENT algorithm frequently extracts the query outcome pages by first classifying and metameric the QRR in the query consequence pages and then bring into line the metameric QRRs into a table, in which the data values from the identical attribute are put into the same column. Experimental results show that our system can achieve high accuracy in distilling and aligning regularly structured objects inside complex web pages. Keywords—Data extraction, automatic wrapper generation, data record arrangement, information integration.