An Efficient Mechanism for Deep Web Data Extraction Based on Tree-Structured Web Pattern Matching. (27th May 2022)

• Record Type: Journal Article
• Authors: Ahamed, B. Bazeer; Yuvaraj, D.; Shitharth, S.; Mizra, Olfat M.; Alsobhi, Aisha; Yafoz, Ayman
• Other Names: Lakshmanna Kuruva Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-05-27
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/6335201
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : The World Wide Web comprises of huge web databases where the data are searched using web query interface. Generally, the World Wide Web maintains a set of databases to store several data records. The distinct data records are extracted by the web query interface as per the user requests. The information maintained in the web database is hidden and retrieves deep web content even in dynamic script pages. In recent days, a web page offers a huge amount of structured data and is in need of various web-related latest applications. The challenge lies in extracting complicated structured data from deep web pages. Deep web contents are generally accessed by the web queries, but extracting the structured data from the web database is a complex problem. Moreover, making use of such retrieved information in combined structures needs significant efforts. No further techniques are established to address the complexity in data extraction of deep web data from various web pages. Despite the fact that several ways for deep web data extraction are offered, very few research address template-related issues at the page level. For effective web data extraction with a large number of online pages, a unique representation of page generation using tree-based pattern matches (TBPM) is proposed. The performance of the proposed technique TBPM is compared to that of existing techniques in terms of relativity, precision, recall, and time consumption. The performance metrics such as high relativity is about 17-26% are achieved when compared to FiVaTech approach.