||This article may be confusing or unclear to readers. (August 2013)|
Web scraping (web harvesting or web data extraction) is a computer software technique of extracting information from websites. Usually, such software programs simulate human exploration of the World Wide Web by either implementing low-level Hypertext Transfer Protocol (HTTP), or embedding a fully-fledged web browser, such as Internet Explorer or Mozilla Firefox.
Web scraping is closely related to web indexing, which indexes information on the web using a bot or web crawler and is a universal technique adopted by most search engines. In contrast, web scraping focuses more on the transformation of unstructured data on the web, typically in HTML format, into structured data that can be stored and analyzed in a central local database or spreadsheet. Web scraping is also related to web automation, which simulates human browsing using computer software. Uses of web scraping include online price comparison, contact scraping, weather data monitoring, website change detection, research, web mashup and web data integration.
A report based on information from the world's largest database for web scraping related activity shows that web scraping related traffic has increased rapidly during the recent years. In average was 23 % of all traffic web scraping related in 2013.
Web scraping is the process of automatically 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. Web scraping, instead, favors practical solutions based on existing technologies that are often entirely ad hoc. Therefore, there are different levels of automation that existing web-scraping technologies can provide:
- Human copy-and-paste: Sometimes even the best web-scraping technology cannot replace a human’s manual examination and copy-and-paste, and sometimes this may be the only workable solution when the websites for scraping explicitly set up barriers to prevent machine automation.
- Text grepping and regular expression matching: A simple yet powerful approach to extract information from web pages can be based on the UNIX grep command or regular expression-matching facilities of programming languages (for instance Perl or Python).
- HTTP programming: Static and dynamic web pages can be retrieved by posting HTTP requests to the remote web server using socket programming.
- HTML parsers: Many websites have large collections of pages generated dynamically from an underlying structured source like a database. Data of the same category are typically encoded into similar pages by a common script or template. In data mining, a program that detects such templates in a particular information source, extracts its content and translates it into a relational form called a wrapper. Wrapper generation algorithms assume that input pages of a wrapper induction system conform to a common template and that they can be easily identified in terms of a URL common scheme. Moreover, some semi-structured data query languages, such as XQuery and the HTQL, can be used to parse HTML pages and to retrieve and transform page content.
- DOM parsing: By embedding a full-fledged web browser, such as the Internet Explorer or the Mozilla browser control, programs can retrieve the dynamic content generated by client-side scripts. These browser controls also parse web pages into a DOM tree, based on which programs can retrieve parts of the pages.
- Web-scraping software: There are many software tools available that can be used to customize web-scraping solutions. This software may attempt to automatically recognize the data structure of a page or provide a recording interface that removes the necessity to manually write web-scraping code, or some scripting functions that can be used to extract and transform content, and database interfaces that can store the scraped data in local databases.
- Vertical aggregation platforms: There are several companies that have developed vertical specific harvesting platforms. These platforms create and monitor a multitude of “bots” for specific verticals with no man-in-the-loop,[clarification needed] and no work related to a specific target site. The preparation involves establishing the knowledge base for the entire vertical and then the platform creates the bots automatically. The platform's robustness is measured by the quality of the information it retrieves (usually number of fields) and its scalability (how quick it can scale up to hundreds or thousands of sites). This scalability is mostly used to target the Long Tail of sites that common aggregators find complicated or too labor-intensive to harvest content from.
- Semantic annotation recognizing: The pages being scraped may embrace metadata or semantic markups and annotations, which can be used to locate specific data snippets. If the annotations are embedded in the pages, as Microformat does, this technique can be viewed as a special case of DOM parsing. In another case, the annotations, organized into a semantic layer, are stored and managed separately from the web pages, so the scrapers can retrieve data schema and instructions from this layer before scraping the pages.
- Computer vision web-page analyzers: There are efforts using machine learning and computer vision that attempt to identify and extract information from web pages by interpreting pages visually as a human being might.
One of the first major tests of screen scraping involved American Airlines, and a firm called FareChase. AA successfully obtained an injunction from a Texas trial court, stopping FareChase from selling software that enables users to compare online fares if it also searches AA's website. The airline argued that FareChase's websearch software trespassed on AA's servers when it collected the publicly available data. FareChase filed an appeal in March 2003. By June, FareChase and AA agreed to settle and the appeal was dropped.
Southwest Airlines has also challenged screen-scraping practices, and has involved both FareChase and another firm, Outtask, in a legal claim. Southwest Airlines charged that the screen-scraping is Illegal since it is an example of "Computer Fraud and Abuse" and has led to "Damage and Loss" and "Unauthorized Access" of Southwest's site. It also constitutes "Interference with Business Relations", "Trespass", and "Harmful Access by Computer". They also claimed that screen-scraping constitutes what is legally known as "Misappropriation and Unjust Enrichment", as well as being a breach of the web site's user agreement. Outtask denied all these claims, claiming that the prevailing law in this case should be US Copyright law, and that under copyright, the pieces of information being scraped would not be subject to copyright protection. Although the cases were never resolved in the Supreme Court of the United States, FareChase was eventually shuttered by parent company Yahoo!, and Outtask was purchased by travel expense company Concur. In 2012, a startup called 3Taps scraped classified housing ads from Craigslist. Craigslist sent 3Taps a cease-and-desist letter and blocked their IP addresses and later sued, in Craigslist v. 3Taps. The court held that the cease-and-desist letter and IP blocking was sufficient for Craigslist to properly claim that 3Taps had violated the Computer Fraud and Abuse Act.
Although these are early scraping decisions, and the theories of liability are not uniform, it is difficult to ignore a pattern emerging that the courts are prepared to protect proprietary content on commercial sites from uses which are undesirable to the owners of such sites. However, the degree of protection for such content is not settled, and will depend on the type of access made by the scraper, the amount of information accessed and copied, the degree to which the access adversely affects the site owner’s system and the types and manner of prohibitions on such conduct.
Outside of the United States, in February 2006, the Danish Maritime and Commercial Court (Copenhagen) ruled that systematic crawling, indexing, and deep linking by portal site ofir.dk of real estate site Home.dk does not conflict with Danish law or the database directive of the European Union.
In 2009 Facebook won one of the first copyright suits against a known web scraper. This laid the groundwork for numerous lawsuits that tie any web scraping with a direct copyright violation and very clear monetary damages. The most recent case being AP v Meltwater, where the courts stripped what is referred to as fair use on the internet.
In a February 2010 case complicated by matters of jurisdiction, Ireland's An Ard-Chúirt delivered a verdict that illustrates the inchoate state of developing case law. In the case of Ryanair Ltd v Billigfluege.de GmbH, Ireland's High Court ruled Ryanair's "click-wrap" agreement to be legally binding. In contrast to the findings of the United States District Court Eastern District of Virginia and those of the Danish Maritime and Commercial Court, Mr. Justice Michael Hanna ruled that the hyperlink to Ryanair's terms and conditions was plainly visible, and that placing the onus on the user to agree to terms and conditions in order to gain access to online services is sufficient to comprise a contractual relationship. Where here may be another legal issue, of data non-valid or incorrect information, as most of data mixed with lot of "Junk" or "Spam". Where with respect to data ware house there are: may be limitation of query interface, inconsistence output and rapid change in data by admin without any notice.  The decision is under appeal in Ireland's Supreme Court, the Cúirt Uachtarach na hÉireann.
Technical measures to stop bots
The administrator of a website can use various measures to stop or slow a bot. Some techniques include:
- Blocking an IP address. This will also block all browsing from that address.
- Disabling any web service API that the website's system might expose.
- Bots sometimes declare who they are (using user agent strings) and can be blocked on that basis (using robots.txt); 'googlebot' is an example. Some bots make no distinction between themselves and a human browser.
- Bots can be blocked by excess traffic monitoring.
- Bots can sometimes be blocked with tools to verify that it is a real person accessing the site, like a CAPTCHA. Bots are sometimes coded to explicitly break specific Captcha patterns.
- Commercial anti-bot services: Companies offer anti-bot and anti-scraping services for websites. A few web application firewalls have limited bot detection capabilities as well.
- Locating bots with a honeypot or other method to identify the IP addresses of automated crawlers.
- Using CSS sprites to display such data as phone numbers or email addresses, at the cost of accessibility to screen reader users.
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- Semantic annotation based web scraping
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