Deep Web (also called the
Deepnet,
Invisible Web, or
Hidden Web) is the portion of World Wide Web content that is not indexed by standard search engines.
Mike Bergman, founder of BrightPlanet and credited with coining the
phrase, said that searching on the Internet today can be compared to
dragging a net across the surface of the ocean: a great deal may be
caught in the net, but there is a wealth of information that is deep and
therefore missed. Most of the Web's information is buried far down on
sites, and standard search engines do not find it. Traditional search
engines cannot see or retrieve content in the deep Web. The portion of
the Web that is indexed by standard search engines is known as the Surface Web. As of 2001, the deep Web was several orders of magnitude larger than the surface Web.
The deep web should not be confused with the dark Internet, computers that can no longer be reached via the Internet. The Darknet distributed file sharing network, can be classified as part of the Deep Web.
Although much of the Deep Web is innocuous, some prosecutors and
government agencies, among others, are concerned that the Deep Web is a
haven for serious criminality.
Size
Bright Planet, a web-services company, describes the size of the Deep Web in this way:
It is impossible to measure or put estimates onto the size of the
deep web because the majority of the information is hidden or locked
inside databases. Early estimates suggested that the deep web is 400 to
550 times larger than the surface web. However, since more information
and sites are always being added, it can be assumed that the deep web is
growing exponentially at a rate that cannot be quantified. Estimates
based on extrapolations from a study done at University of California, Berkeley in 2001 speculate that the deep web consists of about 7.5 petabytes.
More accurate estimates are available for the number of resources in
the deep Web: research of He et al. detected around 300,000 deep web
sites in the entire Web in 2004, and, according to Shestakov, around
14,000 deep web sites existed in the Russian part of the Web in 2006.
Naming
Bergman, in a seminal paper on the deep Web published in
The Journal of Electronic Publishing, mentioned that Jill Ellsworth used the term
invisible Web in 1994 to refer to websites that were not registered with any search engine. Bergman cited a January 1996 article by Frank Garcia:
It would be a site that's possibly reasonably designed, but they
didn't bother to register it with any of the search engines. So, no one
can find them! You're hidden. I call that the invisible Web.
Another early use of the term
Invisible Web was by Bruce Mount
and Matthew B. Koll of Personal Library Software, in a description of
the @1 deep Web tool found in a December 1996 press release.
The first use of the specific term
Deep Web, now generally accepted, occurred in the aforementioned 2001 Bergman study.
Methods
Methods which prevent web pages from being indexed by traditional
search engines may be categorized as one or more of the following:
- Dynamic content: dynamic pages
which are returned in response to a submitted query or accessed only
through a form, especially if open-domain input elements (such as text
fields) are used; such fields are hard to navigate without domain
knowledge.
- Unlinked content: pages which are not linked to by other pages, which may prevent Web crawling programs from accessing the content. This content is referred to as pages without backlinks (also known as inlinks). Also, search engines do not always detect all backlinks from searched web pages.
- Private Web: sites that require registration and login (password-protected resources).
- Contextual Web: pages with content varying for different access
contexts (e.g., ranges of client IP addresses or previous navigation
sequence).
- Limited access content: sites that limit access to their pages in a technical way (e.g., using the Robots Exclusion Standard or CAPTCHAs, or no-store directive which prohibit search engines from browsing them and creating cached copies.)
- Scripted content: pages that are only accessible through links produced by JavaScript as well as content dynamically downloaded from Web servers via Flash or Ajax solutions.
- Non-HTML/text content: textual content encoded in multimedia (image or video) files or specific file formats not handled by search engines.
- Software: Certain content is intentionally hidden from the regular internet, accessible only with special software, such as Tor. Tor allows users to access websites using the .onion host suffix anonymously, hiding their IP address. Other such software includes I2P and darknet software.
Indexing the Deep Web
While it is not always possible to directly discover a specific web
server's content so that it may be indexed, a site potentially can be
accessed indirectly (due to computer vulnerabilities).
To discover content on the Web, search engines use web crawlers that follow hyperlinks through known protocol virtual port numbers. This technique is ideal for discovering content on the surface Web
but is often ineffective at finding Deep Web content. For example,
these crawlers do not attempt to find dynamic pages that are the result
of database queries due to the indeterminate number of queries that are
possible. It has been noted that this can be (partially) overcome by
providing links to query results, but this could unintentionally inflate
the popularity for a member of the deep Web.
DeepPeep, Intute, Deep Web Technologies, Scirus,
and Ahmia.fi are a few search engines that have accessed the Deep Web.
Intute ran out of funding and is now a temporary static archive as of
July, 2011. Scirus retired near the end of January, 2013.
Researchers have been exploring how the Deep Web can be crawled in an
automatic fashion, including content that can be accessed only by
special software such as Tor. In 2001, Sriram Raghavan and Hector
Garcia-Molina (Stanford Computer Science Department, Stanford
University) presented an architectural model for a hidden-Web crawler
that used key terms provided by users or collected from the query
interfaces to query a Web form and crawl the Deep Web content.
Alexandros Ntoulas, Petros Zerfos, and Junghoo Cho of UCLA
created a hidden-Web crawler that automatically generated meaningful
queries to issue against search forms. Several form query languages
(e.g., DEQUEL) have been proposed that, besides issuing a query, also
allow extraction of structured data from result pages. Another effort is
DeepPeep, a project of the University of Utah sponsored by the National Science Foundation, which gathered hidden-Web sources (Web forms) in different domains based on novel focused crawler techniques.
Commercial search engines have begun exploring alternative methods to crawl the deep Web. The Sitemap Protocol (first developed, and introduced by Google in 2005) and mod oai
are mechanisms that allow search engines and other interested parties
to discover deep Web resources on particular Web servers. Both
mechanisms allow Web servers to advertise the URLs that are accessible
on them, thereby allowing automatic discovery of resources that are not
directly linked to the surface Web. Google's deep Web surfacing system
pre-computes submissions for each HTML form and adds the resulting HTML
pages into the Google search engine index. The surfaced results account
for a thousand queries per second to deep Web content. In this system,
the pre-computation of submissions is done using three algorithms:
- selecting input values for text search inputs that accept keywords,
- identifying inputs which accept only values of a specific type (e.g., date), and
- selecting a small number of input combinations that generate URLs suitable for inclusion into the Web search index.
In 2008, to facilitate users of Tor hidden services in their access and search of a hidden .onion suffix, Aaron Swartz designed Tor2web—a
proxy application able to provide access by means of common web
browsers. Using this application, Deep Web links appear as a random
string of letters followed by the .onion TLD. For example, http://xmh57jrzrnw6insl followed by .onion, links to TORCH, the Tor search engine web page.
Classifying resources
Most of the work of classifying search results has been in
categorizing the surface Web by topic. For classification of deep Web
resources, Ipeirotis
et al. presented an algorithm that classifies a deep Web site into the
category that generates the largest number of hits for some carefully
selected, topically-focused queries. Deep Web directories under
development include OAIster at the University of Michigan, Intute at the University of Manchester, Infomine at the University of California at Riverside, and DirectSearch (by Gary Price).
This classification poses a challenge while searching the deep Web
whereby two levels of categorization are required. The first level is to
categorize sites into vertical topics (e.g., health, travel,
automobiles) and sub-topics according to the nature of the content
underlying their databases.
The more difficult challenge is to categorize and map the information
extracted from multiple deep Web sources according to end-user needs.
Deep Web search reports cannot display URLs like traditional search
reports. End users expect their search tools to not only find what they
are looking for, but to be intuitive and user-friendly. In order to be
meaningful, the search reports have to offer some depth to the nature of
content that underlie the sources or else the end-user will be lost in
the sea of URLs that do not indicate what content lies beneath them. The
format in which search results are to be presented varies widely by the
particular topic of the search and the type of content being exposed.
The challenge is to find and map similar data elements from multiple
disparate sources so that search results may be exposed in a unified
format on the search report irrespective of their source.
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