WEIDJ: Development of a new algorithm for semi-structured web data extraction

Abstract

In the era of industrial digitalization, people are increasingly investing in solutions that allow their process for data collection, data analysis and performance improvement. In this paper, advancing web scale knowledge extraction and alignment by integrating few sources by exploring different methods of aggregation and attention is considered in order focusing on image information. The main aim of data extraction with regards to semistructured data is to retrieve beneficial information from the web. The data from web also known as deep web is retrievable but it requires request through form submission because it cannot be performed by any search engines. 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. In this research work, we propose an improved model namely wrapper extraction of image using document object model (DOM) and JavaScript object notation data (JSON) (WEIDJ) in response to the promising results of mining in a higher volume of image from a various type of format. To observe the efficiency of WEIDJ, we compare the performance of data extraction by different level of page extraction with 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.

A deep web data extraction model for web mining: a review

“…This method is useful in handling the structure of data, whether it is structured, semi-structured or unstructured. The second part is related to the knowledge based 1 shows general models for three web data extraction models; DOM [23], WHDJ [24] and WEIDJ [25]. In addition to the basic capabilities of WEIDJ, our extractor also provides several other useful and user's friendly features.…”

“…The image extraction has been extracted in three ways: a) The extraction of images in general way b) The extraction of images by considering the size of images in two parts; 50*50 pixels and 128*128 pixels. c) The extraction of images is tested randomly at different levels; 5 pages, 10 pages, 15 pages, 20 pages, 25 pages and 30 pages.…”

Big Data Bot with a Special Reference to Bioinformatics

“…International Data Corporation stated that unstructured data would make up 95% of all data worldwide in 2020, with a compound annual growth rate of 65% [30]. Due to the quality and usability concerns with large unstructured datasets, structured data are more relevant and valuable than unstructured or semi-structured data [31]. It has been stated that all prospective big data solutions are hampered by the unstructured nature of data, which have no schema, many formats, originates from various sources, and lacks standards [32].…”

“…However, in some cases, these data extraction tools and methods do not offer data transformation to convert the data to a structured form. Based on this, efforts are needed to deal with this problem and convert the unstructured form of data into more meaningful and structured information [10,31,39]. The development for automating the extraction of semi-structured web data is much needed.…”