Big Data Analysis of Web Data Extraction

Abstract

In this study, the large data extraction techniques; include detection of patterns and secret relationships between factors numbering and bring in the required information. Rapid analysis of massive data can lead to innovation and concepts of the theoretical value. Compared with results from mining between traditional data sets and the vast amount of large heterogeneous data interdependent it has the ability expand the knowledge and ideas about the target domain. We studied in this research data mining on the Internet. The various networks that are used to extract data onto different locations complex may appear sometimes and has been used to extract information on the web technology to extract and data analysis (Marwah et al., 2016). In this research, we extracted the information on large quantities of the web pages and examined the pages of the site using Java code, and we added the extracted information on a special database for the web page. We used the data network function to get accurate results of evaluating and categorizing the data pages found, which identifies the trusted web or risky web pages, and imported the data onto a CSV extension. Consequently, examine and categorize these data using WEKA to obtain accurate results. We concluded from the results that the applied data mining algorithms are better than other techniques in classification and extraction of data and high performance.