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The complete guide on web crawling techniques
Web crawling is booming from being an evolving technology to an important part of many businesses. The first crawlers were developed for a much smaller web, but today some of the popular sites alone have millions of pages. There are many processes for this, which is a combination of different levels of crawling.
What are the various crawling techniques?
Selective Crawling is the process to retrieve web pages based on specific criteria. We are using a scoring function to determine relevant content from a page. The fetched URLs are sorted according to a relevance score from the scoring function. Best-first search is the technique used to obtain pages with a high scos search leads to the most re first. Thirelevant pages.
Following are some of the examples of scoring functions and their criteria are given below:
Length of the path from the site’s homepage to the document.
Limit the total number of levels retrieved from a site.
Maximize coverage breadth.
Assign relevance according to which pages are more important than others.
Estimate the number of backlinks.
Focused Crawling is the process to fetch pages within a certain topic. Crawlers will download pages that are related to each other. It collects documents which are specific and relevant to the given topic and classify crawled pages into categories. It determines how far the given page is relevant to the particular topic and how to proceed forward.
Focused Crawling is also known as Topic Crawler because of the way it works. It is economically feasible in terms of hardware and network resources. It can also reduce the amount of network traffic and downloads. The search exposure of a focused web crawler is relatively huge.
Read also: Crawl without getting busted!!!!
Distributed crawling is the process of the partitioning of tasks or it is similar to distributed computing technique. A central server manages the communication and synchronization of the nodes, as it is geographically distributed. It basically uses the PageRank algorithm for its increased efficiency and quality search. The advantage of a distributed web crawler is that it is robust against system crashes and other events. It is more scalable and memory efficient. It also has increased overall download speed and reliability.
Incremental Crawling is the traditional crawling or the process of prioritizing and revisiting URLs to refresh its collection. It achieves so by periodically replacing the old documents with the newly downloaded documents. The incremental crawler gradually refreshes the existing collection of pages by visiting them frequently. It does so based on the estimate of how often the pages change. It also exchanges less important pages by new and more important pages. It also resolves the problem of content consistency. The benefit of an incremental crawler is that only the valuable data is provided to the user. Thus, the network bandwidth is saved, and data enrichment is achieved.
Parallel Crawling is the process that runs multiple processes in parallel. That process is called C-procs which can run on a network of workstations. The Parallel crawlers depend on Page freshness and Page selection. A Parallel crawler can be on a local network or can be distributed at geographically distant locations. Parallelization of the crawling system is very vital from the point of view of downloading documents in a reasonable amount of time.
Web Dynamics is the rate of change of information on the Web. It is mainly used by search engines for updating the index. The index entry for a certain document, indexed at time t0 is said ṣto be β- current at time t if the document has not changed in the time interval between t0 and t − β, where β is a ‘grace period’. If we pretend that the user query was made β time units ago rather than now, then the information in the search engine would be up to date. A search engine for a given collection of documents is said to be "(α, β)-current" if the probability of a document being "β-current" is at least α. According to this definition, we can ask questions like ‘how many documents per day should a search engine refresh to guarantee it will remain (0.9,1 week)-current?’ Answering this question requires that we develop a probabilistic model of Web dynamics.