Deep Web
The deep Web (also called Deepnet, the invisible Web, dark Web or the hidden Web) refers to World Wide Web content that is not part of the surface Web, which is indexed by standard search engines.
Mike Bergman, credited with coining the phrase, has 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 dynamically generated sites, and standard search engines do not find it. Traditional search engines cannot "see" or retrieve content in the deep Web – those pages do not exist until they are created dynamically as the result of a specific search. The deep Web is several orders of magnitude larger than the surface Web.
Naming
Bergman, in a seminal, early 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 are 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.
Size
In 2000, it was estimated that the deep Web contained approximately 7,500 terabytes of data and 550 billion individual documents. Estimates based on extrapolations from a study done at University of California, Berkeley, show that the deep Web consists of about 91,000 terabytes. By contrast, the surface Web (which is easily reached by search engines) is only about 167 terabytes; the Library of Congress, in 1997, was estimated to have perhaps 3,000 terabytes.
Deep resources
Deep Web resources may be classified into one or more of the following categories:
* 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 (or inlinks).
* 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, CAPTCHAs, or no-cache Pragma HTTP headers 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.
Accessing
To discover content on the Web, search engines use web crawlers that follow hyperlinks. This technique is ideal for discovering resources on the surface Web but is often ineffective at finding deep Web resources. For example, these crawlers do not attempt to find dynamic pages that are the result of database queries due to the infinite 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 (e.g., PageRank) for a member of the deep Web.
One way to access the deep Web is via federated search based search engines. Search tools such as Science.gov are being designed to retrieve information from the deep Web. These tools identify and interact with searchable databases, aiming to provide access to deep Web content.
Another way to explore the deep Web is by using human crawlers instead of algorithmic crawlers. In this paradigm, referred to as Web harvesting, humans find interesting links of the deep Web that algorithmic crawlers can't find. This human-based computation technique to discover the deep Web has been used by the StumbleUpon service since February 2002.
In 2005, Yahoo! made a small part of the deep Web searchable by releasing Yahoo! Subscriptions. This search engine searches through a few subscription-only Web sites. Some subscription websites display their full content to search engine robots so they will show up in user searches, but then show users a login or subscription page when they click a link from the search engine results page.
Crawling the deep Web
Researchers have been exploring how the deep Web can be crawled in an automatic fashion. In 2001, Sriram Raghavan and Hector Garcia-Molina 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 resources. Alexandros Ntoulas, Petros Zerfos, and Junghoo Cho of UCLA created a hidden-Web crawler that automatically generated meaningful queries to issue against search forms.Their crawler generated promising results, but the problem is far from being solved, as the authors recognized. 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 by Google) 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: (1) selecting input values for text search inputs that accept keywords, (2) identifying inputs which accept only values of a specific type (e.g., date), and (3) selecting a small number of input combinations that generate URLs suitable for inclusion into the Web search index.
Classifying resources
It is difficult to automatically determine if a Web resource is a member of the surface Web or the deep Web. If a resource is indexed by a search engine, it is not necessarily a member of the surface Web, because the resource could have been found using another method (e.g., the Sitemap Protocol, mod oai, OAIster) instead of traditional crawling. If a search engine provides a backlink for a resource, one may assume that the resource is in the surface Web. Unfortunately, search engines do not always provide all backlinks to resources. Even if a backlink does exist, there is no way to determine if the resource providing the link is itself in the surface Web without crawling all of the Web. Furthermore, a resource may reside in the surface Web, but it has not yet been found by a search engine. Therefore, if we have an arbitrary resource, we cannot know for sure if the resource resides in the surface Web or deep Web without a complete crawl of the Web.
The concept of classifying search results by topic was pioneered by Yahoo! Directory search[citation needed] and is gaining importance as search becomes more relevant in day-to-day decisions. However, most of the work here 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 as 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 quickly, 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 underneath 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.
Future
The lines between search engine content and the deep Web have begun to blur, as search services start to provide access to part or all of once-restricted content. An increasing amount of deep Web content is opening up to free search as publishers and libraries make agreements with large search engines. In the future, deep Web content may be defined less by opportunity for search than by access fees or other types of authentication.
Content on the deep Web
When we refer to the deep Web, we are usually talking about the following:
* The content of databases. Databases contain information stored in tables created by such programs as Access, Oracle, SQL Server, and MySQL. (There are other types of databases, but we will focus on database tables for the sake of simplicity.) Information stored in databases is accessible only by query. In other words, the database must somehow be searched and the data retrieved and then displayed on a Web page. This is distinct from static, self-contained Web pages, which can be accessed directly. A significant amount of valuable information on the Web is generated from databases.
* Non-text files such as multimedia, images, software, and documents in formats such as Portable Document Format (PDF). For example, see Digital Image Resources on the Deep Web for a good indication of what is out there for images.
* Content available on sites protected by passwords or other restrictions. Some of this is fee-based content, such as subscription content paid for by libraries and available to their users based on various authentication schemes.
* Special content not presented as Web pages, such as full text articles and books
* Dynamically-changing, updated content
This is usually the basic,"traditional" list. In these days of the social Web, let's consider adding new content to our list of deep Web sources. For example:
* Blog postings
* Comments
* Discussions and other communicative activities on social networking sites
* Bookmarks and citations stored on social bookmarking sites
As you can see, based on these few examples, the deep Web is expanding.
Tips for dealing with deep Web content
* Vertical search can solve some of the problems with the deep Web. With vertical search, you can query an index or database focused on a specific topic, industry, type of content, geographical location, language, file type, Web site, piece of data, and so on. For example, consider MedNar and PubMed to search for medical topics. On the social Web, there are search engines for blogs, RSS feeds, Twitter content, and so on. See the tutorial on Vertical Search Engines for more information.
* Use a general search engine to search for a vertical search engine. For example, a Google search on "stock market search" will retrieve sites that allow you to search for current stock prices, market news, etc. This may be thought of as split level searching. For the first level, search for the database site. For the second level, go to the site and search the database itself for the information you want.
* A number of general search engines will search the deep Web for related content subsequent to an initial search. For example, try a search on Google for "World Trade Center" and select the Images tab. This will retrieve many pages of images of the World Trade Center. Look for this type of feature on other search engines.
* Try to figure out which kind of information might be stored in a database.. There is no general rule. But think about large listings of things with a common theme. A few examples of databased content include:
* phone books
* "people finders" such as lists of professionals such as doctors or lawyers
* patents
* laws
* dictionary definitions
* items for sale in a Web store or on Web-based auctions
* digital exhibits
* images and multimedia
* full text articles and books
* Information that is new and dynamically changing in content will appear on the deep Web. Look to the deep Web for late breaking items, such as:
* news
* job postings
* available airline flights, hotel rooms
* stock and bond prices, market averages
* The social Web often jumps on a late-breaking situation with news items and commentary. Blogs, Twitter, and other social networking environments sometimes get out the word before more traditional sources.
* Topical coverage on the deep Web is extremely varied. This presents a challenge, since it is impossible to anticipate what might turn up.
These limitations are, however, being overcome by the new search engine crawlers (like Pipl) being designed today. These new crawlers are designed to identify, interact and retrieve information from deep web resources and searchable databases. Google, for example, has developed the mod oai and Sitemap Protocol in order to increase results from deep web searches of web servers. These new developments will allow the web servers to automatically show the URLs that they can access to search engines.
Another solution that is being developed by several search engines like Alacra, Northern Light and CloserLookSearch are specialty search engines that focus only in particular topics or subject areas. This would allow the search engines to narrow their search and make a more in-depth search of the deep web by querying password-protected and dynamic databases.
The deep Web (also called Deepnet, the invisible Web, dark Web or the hidden Web) refers to World Wide Web content that is not part of the surface Web, which is indexed by standard search engines.Mike Bergman, credited with coining the phrase, has 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 dynamically generated sites, and standard search engines do not find it. Traditional search engines cannot "see" or retrieve content in the deep Web – those pages do not exist until they are created dynamically as the result of a specific search. The deep Web is several orders of magnitude larger than the surface Web.
Naming
Bergman, in a seminal, early 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 are 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.
Size
In 2000, it was estimated that the deep Web contained approximately 7,500 terabytes of data and 550 billion individual documents. Estimates based on extrapolations from a study done at University of California, Berkeley, show that the deep Web consists of about 91,000 terabytes. By contrast, the surface Web (which is easily reached by search engines) is only about 167 terabytes; the Library of Congress, in 1997, was estimated to have perhaps 3,000 terabytes.
Deep resources
Deep Web resources may be classified into one or more of the following categories:
* 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 (or inlinks).
* 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, CAPTCHAs, or no-cache Pragma HTTP headers 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.
Accessing
To discover content on the Web, search engines use web crawlers that follow hyperlinks. This technique is ideal for discovering resources on the surface Web but is often ineffective at finding deep Web resources. For example, these crawlers do not attempt to find dynamic pages that are the result of database queries due to the infinite 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 (e.g., PageRank) for a member of the deep Web.
One way to access the deep Web is via federated search based search engines. Search tools such as Science.gov are being designed to retrieve information from the deep Web. These tools identify and interact with searchable databases, aiming to provide access to deep Web content.
Another way to explore the deep Web is by using human crawlers instead of algorithmic crawlers. In this paradigm, referred to as Web harvesting, humans find interesting links of the deep Web that algorithmic crawlers can't find. This human-based computation technique to discover the deep Web has been used by the StumbleUpon service since February 2002.
In 2005, Yahoo! made a small part of the deep Web searchable by releasing Yahoo! Subscriptions. This search engine searches through a few subscription-only Web sites. Some subscription websites display their full content to search engine robots so they will show up in user searches, but then show users a login or subscription page when they click a link from the search engine results page.
Crawling the deep Web
Researchers have been exploring how the deep Web can be crawled in an automatic fashion. In 2001, Sriram Raghavan and Hector Garcia-Molina 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 resources. Alexandros Ntoulas, Petros Zerfos, and Junghoo Cho of UCLA created a hidden-Web crawler that automatically generated meaningful queries to issue against search forms.Their crawler generated promising results, but the problem is far from being solved, as the authors recognized. 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 by Google) 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: (1) selecting input values for text search inputs that accept keywords, (2) identifying inputs which accept only values of a specific type (e.g., date), and (3) selecting a small number of input combinations that generate URLs suitable for inclusion into the Web search index.
Classifying resources
It is difficult to automatically determine if a Web resource is a member of the surface Web or the deep Web. If a resource is indexed by a search engine, it is not necessarily a member of the surface Web, because the resource could have been found using another method (e.g., the Sitemap Protocol, mod oai, OAIster) instead of traditional crawling. If a search engine provides a backlink for a resource, one may assume that the resource is in the surface Web. Unfortunately, search engines do not always provide all backlinks to resources. Even if a backlink does exist, there is no way to determine if the resource providing the link is itself in the surface Web without crawling all of the Web. Furthermore, a resource may reside in the surface Web, but it has not yet been found by a search engine. Therefore, if we have an arbitrary resource, we cannot know for sure if the resource resides in the surface Web or deep Web without a complete crawl of the Web.
The concept of classifying search results by topic was pioneered by Yahoo! Directory search[citation needed] and is gaining importance as search becomes more relevant in day-to-day decisions. However, most of the work here 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 as 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 quickly, 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 underneath 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.
Future
The lines between search engine content and the deep Web have begun to blur, as search services start to provide access to part or all of once-restricted content. An increasing amount of deep Web content is opening up to free search as publishers and libraries make agreements with large search engines. In the future, deep Web content may be defined less by opportunity for search than by access fees or other types of authentication.
Content on the deep Web
When we refer to the deep Web, we are usually talking about the following:
* The content of databases. Databases contain information stored in tables created by such programs as Access, Oracle, SQL Server, and MySQL. (There are other types of databases, but we will focus on database tables for the sake of simplicity.) Information stored in databases is accessible only by query. In other words, the database must somehow be searched and the data retrieved and then displayed on a Web page. This is distinct from static, self-contained Web pages, which can be accessed directly. A significant amount of valuable information on the Web is generated from databases.
* Non-text files such as multimedia, images, software, and documents in formats such as Portable Document Format (PDF). For example, see Digital Image Resources on the Deep Web for a good indication of what is out there for images.
* Content available on sites protected by passwords or other restrictions. Some of this is fee-based content, such as subscription content paid for by libraries and available to their users based on various authentication schemes.
* Special content not presented as Web pages, such as full text articles and books
* Dynamically-changing, updated content
This is usually the basic,"traditional" list. In these days of the social Web, let's consider adding new content to our list of deep Web sources. For example:
* Blog postings
* Comments
* Discussions and other communicative activities on social networking sites
* Bookmarks and citations stored on social bookmarking sites
As you can see, based on these few examples, the deep Web is expanding.
Tips for dealing with deep Web content
* Vertical search can solve some of the problems with the deep Web. With vertical search, you can query an index or database focused on a specific topic, industry, type of content, geographical location, language, file type, Web site, piece of data, and so on. For example, consider MedNar and PubMed to search for medical topics. On the social Web, there are search engines for blogs, RSS feeds, Twitter content, and so on. See the tutorial on Vertical Search Engines for more information.
* Use a general search engine to search for a vertical search engine. For example, a Google search on "stock market search" will retrieve sites that allow you to search for current stock prices, market news, etc. This may be thought of as split level searching. For the first level, search for the database site. For the second level, go to the site and search the database itself for the information you want.
* A number of general search engines will search the deep Web for related content subsequent to an initial search. For example, try a search on Google for "World Trade Center" and select the Images tab. This will retrieve many pages of images of the World Trade Center. Look for this type of feature on other search engines.
* Try to figure out which kind of information might be stored in a database.. There is no general rule. But think about large listings of things with a common theme. A few examples of databased content include:
* phone books
* "people finders" such as lists of professionals such as doctors or lawyers
* patents
* laws
* dictionary definitions
* items for sale in a Web store or on Web-based auctions
* digital exhibits
* images and multimedia
* full text articles and books
* Information that is new and dynamically changing in content will appear on the deep Web. Look to the deep Web for late breaking items, such as:
* news
* job postings
* available airline flights, hotel rooms
* stock and bond prices, market averages
* The social Web often jumps on a late-breaking situation with news items and commentary. Blogs, Twitter, and other social networking environments sometimes get out the word before more traditional sources.
* Topical coverage on the deep Web is extremely varied. This presents a challenge, since it is impossible to anticipate what might turn up.
These limitations are, however, being overcome by the new search engine crawlers (like Pipl) being designed today. These new crawlers are designed to identify, interact and retrieve information from deep web resources and searchable databases. Google, for example, has developed the mod oai and Sitemap Protocol in order to increase results from deep web searches of web servers. These new developments will allow the web servers to automatically show the URLs that they can access to search engines.
Another solution that is being developed by several search engines like Alacra, Northern Light and CloserLookSearch are specialty search engines that focus only in particular topics or subject areas. This would allow the search engines to narrow their search and make a more in-depth search of the deep web by querying password-protected and dynamic databases.
