Web Data Extraction Approach for Deep Web using WEIDJ

• Main Authors: Wan Aezwani, Wan Abu Bakar, Ahmad Nazari, Mohd Rose
• Format: Conference or Workshop Item
• Language: English
• Published: 2019
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software
• Online Access: http://eprints.unisza.edu.my/1870/1/FH03-FIK-20-37010.pdf; http://eprints.unisza.edu.my/1870/

Data extraction is one of the most prominent areas in data mining analysis that is been extensively studied especially in the field of data requirements and reservoir. The main aim of data extraction with regards to semi-structured data is to retrieve beneficial information from the World Wide Web. The data from large web data also known as deep web is retrievable but it requires request through form submission because it cannot be performed by any search engines. Data mining applications and automatic data extraction are very cumbersome due to the diverse structure of web pages. Most of the previous data extraction techniques were dealing with various data types such as text, audio, video and etc. but research works that are focusing on image as data are still lacking. Document Object Model (DOM) is an example of the state of the art of data extraction technique that is related to research work in mining image data. DOM was the method used to solve semi-structured data extraction from web. However, as the HTML documents start to grow larger, it has been found that the process of data extraction has been plagued with lengthy processing time and noisy information. In this research work, we propose an improved model namely Wrapper Extraction of Image using DOM and JSON (WEIDJ) in response to the promising results of mining in a higher volume of web data from a various types of image format and taking the consideration of web data extraction from deep web. To observe the efficiency of the proposed model, we compare the performance of data extraction by different level of page extraction with existing methods such as VIBS, MDR, DEPTA and VIDE. It has yielded the best results in Precision with 100, Recall with 97.93103 and F-measure with 98.9547.