A Brief History of Web Crawlers

Web crawlers visit internet applications, collect data, and learn about new web pages from visited pages. Web crawlers have a long and interesting history. Early web crawlers collected statistics about the web. In addition to collecting statistics about the web and indexing the applications for search engines, modern crawlers can be used to perform accessibility and vulnerability checks on the application. Quick expansion of the web, and the complexity added to web applications have made the process of crawling a very challenging one. Throughout the history of web crawling many researchers and industrial groups addressed different issues and challenges that web crawlers face. Different solutions have been proposed to reduce the time and cost of crawling. Performing an exhaustive crawl is a challenging question. Additionally capturing the model of a modern web application and extracting data from it automatically is another open question. What follows is a brief history of different technique and algorithms used from the early days of crawling up to the recent days. We introduce criteria to evaluate the relative performance of web crawlers. Based on these criteria we plot the evolution of web crawlers and compare their performanc

A Novel Web Scraping Approach Using the Additional Information Obtained from Web Pages

Web scraping is a process of extracting valuable and interesting text information from web pages. Most of the current studies targeting this task are mostly about automated web data extraction. In the extraction process, these studies first create a DOM tree and then access the necessary data through this tree. The construction process of this tree increases the time cost depending on the data structure of the DOM Tree. In the current web scraping literature, it is observed that time efficiency is ignored. This study proposes a novel approach, namely UzunExt, which extracts content quickly using the string methods and additional information without creating a DOM Tree. The string methods consist of the following consecutive steps: searching for a given pattern, then calculating the number of closing HTML elements for this pattern, and finally extracting content for the pattern. In the crawling process, our approach collects the additional information, including the starting position for enhancing the searching process, the number of inner tag for improving the extraction process, and tag repetition for terminating the extraction process. The string methods of this novel approach are about 60 times faster than extracting with the DOM-based method. Moreover, using these additional information improves extraction time by 2.35 times compared to using only the string methods. Furthermore, this approach can easily be adapted to other DOM-based studies/parsers in this task to enhance their time efficiencies. © 2013 IEEE

Bot crawler to retrieve data from Facebook based on the selection of posts and the extraction of user profiles

Introduction: Data can currently be found within organizations and outside of them, they are growing exponentially. Today, the information available on the Internet and social networks has become a generator of value, through the effective analysis of a specific situation, using techniques and methodologies with which content-based solutions can be proposed, and thus achieve, execute timely, intelligent and assertive decision-making processes. Objective: The main objective of this work is to development of a Bot Crawler, which allows extracting information from Facebook without access restrictions, or request for credentials, based on web crawling and scraping techniques, through the selection of HTML tags, to track and be able to define patterns. Method: The development of this project consisted of four main stages: A) Teamwork with SCRUM, B) Comparison of web data extraction techniques, C) Extraction and validation of permissions to access the data in Facebook, D) Development of the bor crawler. Results:  Briefly, mention the main results of the research Conclusions: As a result of this process, a graphical interface is created that allows checking the process of obtaining data derived from user profiles of this social network

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

BlogForever D2.4: Weblog spider prototype and associated methodology

The purpose of this document is to present the evaluation of different solutions for capturing blogs, established methodology and to describe the developed blog spider prototype