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The concepts of topical and focused crawling were first introduced by Filippo Menczer [20] [21] and by Soumen Chakrabarti et al. [22] The main problem in focused crawling is that in the context of a Web crawler, we would like to be able to predict the similarity of the text of a given page to the query before actually downloading the page.
Some of the techniques for indexing, and caching are trade secrets, whereas web crawling is a straightforward process of visiting all sites on a systematic basis. Between visits by the spider , the cached version of the page (some or all the content needed to render it) stored in the search engine working memory is quickly sent to an inquirer.
Web scraping is the process of automatically mining data or collecting information from the World Wide Web. It is a field with active developments sharing a common goal with the semantic web vision, an ambitious initiative that still requires breakthroughs in text processing, semantic understanding, artificial intelligence and human-computer interactions.
In addition, ontologies can be automatically updated in the crawling process. Dong et al. [15] introduced such an ontology-learning-based crawler using support vector machine to update the content of ontological concepts when crawling Web Pages. Crawlers are also focused on page properties other than topics.
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Scrapy (/ ˈ s k r eɪ p aɪ / [2] SKRAY-peye) is a free and open-source web-crawling framework written in Python. Originally designed for web scraping, it can also be used to extract data using APIs or as a general-purpose web crawler. [3] It is currently maintained by Zyte (formerly Scrapinghub), a web-scraping development and services company.
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Distributed web crawling is a distributed computing technique whereby Internet search engines employ many computers to index the Internet via web crawling.Such systems may allow for users to voluntarily offer their own computing and bandwidth resources towards crawling web pages.