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The latter returns the bulk of the listings. Crawlers work by recording every hypertext link in every page they index crawling. Like ripples propagating across a pond, search-engine crawlers are able to extend their indices further and further from their starting points. The surface Web contains an estimated 2.
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Legitimate criticism has been leveled against search engines for these indiscriminate crawls, mostly because they provide too many results search on "Web," for example, with Northern Light, and you will get about 47 million hits. Also, because new documents are found from links within other documents, those documents that are cited are more likely to be indexed than new documents — up to eight times as likely. To overcome these limitations, the most recent generation of search engines notably Google have replaced the random link-following approach with directed crawling and indexing based on the "popularity" of pages.
In this approach, documents more frequently cross-referenced than other documents are given priority both for crawling and in the presentation of results.
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This approach provides superior results when simple queries are issued, but exacerbates the tendency to overlook documents with few links. And, of course, once a search engine needs to update literally millions of existing Web pages, the freshness of its results suffer. Numerous commentators have noted the increased delay in posting and recording new information on conventional search engines. Moreover, return to the premise of how a search engine obtains its listings in the first place, whether adjusted for popularity or not.
That is, without a linkage from another Web document, the page will never be discovered. But the main failing of search engines is that they depend on the Web's linkages to identify what is on the Web. Figure 1 is a graphical representation of the limitations of the typical search engine. The content identified is only what appears on the surface and the harvest is fairly indiscriminate. There is tremendous value that resides deeper than this surface content.
The information is there, but it is hiding beneath the surface of the Web. How does information appear and get presented on the Web? In the earliest days of the Web, there were relatively few documents and sites. It was a manageable task to post all documents as static pages. Because all pages were persistent and constantly available, they could be crawled easily by conventional search engines.
In July , the Lycos search engine went public with a catalog of 54, documents. Sites that were required to manage tens to hundreds of documents could easily do so by posting fixed HTML pages within a static directory structure. However, beginning about , three phenomena took place. Second, the Web became commercialized initially via directories and search engines, but rapidly evolved to include e-commerce.
This confluence produced a true database orientation for the Web, particularly for larger sites. It is now accepted practice that large data producers such as the U. Census Bureau , Securities and Exchange Commission , and Patent and Trademark Office , not to mention whole new classes of Internet-based companies, choose the Web as their preferred medium for commerce and information transfer. What has not been broadly appreciated, however, is that the means by which these entities provide their information is no longer through static pages but through database-driven designs.
It has been said that what cannot be seen cannot be defined, and what is not defined cannot be understood. Such has been the case with the importance of databases to the information content of the Web. And such has been the case with a lack of appreciation for how the older model of crawling static Web pages — today's paradigm for conventional search engines — no longer applies to the information content of the Internet.
In , Dr. Jill Ellsworth first coined the phrase "invisible Web" to refer to information content that was "invisible" to conventional search engines. For this study, we have avoided the term "invisible Web" because it is inaccurate. The only thing "invisible" about searchable databases is that they are not indexable nor able to be queried by conventional search engines. Using BrightPlanet technology, they are totally "visible" to those who need to access them. Figure 2 represents, in a non-scientific way, the improved results that can be obtained by BrightPlanet technology. By first identifying where the proper searchable databases reside, a directed query can then be placed to each of these sources simultaneously to harvest only the results desired — with pinpoint accuracy.
Additional aspects of this representation will be discussed throughout this study. For the moment, however, the key points are that content in the deep Web is massive — approximately times greater than that visible to conventional search engines — with much higher quality throughout. BrightPlanet's technology is uniquely suited to tap the deep Web and bring its results to the surface. The simplest way to describe our technology is a "directed-query engine.
Like any newly discovered phenomenon, the deep Web is just being defined and understood. Daily, as we have continued our investigations, we have been amazed at the massive scale and rich content of the deep Web. This white paper concludes with requests for additional insights and information that will enable us to continue to better understand the deep Web. This paper does not investigate non-Web sources of Internet content.
This study also purposely ignores private intranet information hidden behind firewalls. Many large companies have internal document stores that exceed terabytes of information.
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Since access to this information is restricted, its scale can not be defined nor can it be characterized. We do, however, include those codes in our quantification of total content see next section. Finally, the estimates for the size of the deep Web include neither specialized search engine sources — which may be partially "hidden" to the major traditional search engines — nor the contents of major search engines themselves. This latter category is significant. Simply accounting for the three largest search engines and average Web document sizes suggests search-engine contents alone may equal 25 terabytes or more  or somewhat larger than the known size of the surface Web.
All deep-Web and surface-Web size figures use both total number of documents or database records in the case of the deep Web and total data storage. Use of this standard convention allows apples-to-apples size comparisons between the surface and deep Web. The HTML-included convention was chosen because:.
In actuality, data storage from deep-Web documents will therefore be considerably less than the figures reported. While including this HTML code content overstates the size of searchable databases, standard "static" information on the surface Web is presented in the same manner. HTML-included Web page comparisons provide the common denominator for comparing deep and surface Web sources.
All retrievals, aggregations, and document characterizations in this study used BrightPlanet's technology.
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The technology uses multiple threads for simultaneous source queries and then document downloads. It completely indexes all documents retrieved including HTML content. After being downloaded and indexed, the documents are scored for relevance using four different scoring algorithms, prominently vector space modeling VSM and standard and modified extended Boolean information retrieval EBIR.
Automated deep Web search-site identification and qualification also used a modified version of the technology employing proprietary content and HTML evaluation methods. Their analyses are based on what they term the "publicly indexable" Web. Their first major study, published in Science magazine in , using analysis from December , estimated the total size of the surface Web as million documents. In partnership with Inktomi, NEC updated its Web page estimates to one billion documents in early These are the baseline figures used for the size of the surface Web in this paper.
A more recent study from Cyveillance [5e] has estimated the total surface Web size to be 2. This is likely a more accurate number, but the NEC estimates are still used because they were based on data gathered closer to the dates of our own analysis. More than individual deep Web sites were characterized to produce the listing of sixty sites reported in the next section. Estimating total record count per site was often not straightforward. A series of tests was applied to each site and are listed in descending order of importance and confidence in deriving the total document count:. An initial pool of 53, possible deep Web candidate URLs was identified from existing compilations at seven major sites and three minor ones.
Cursory inspection indicated that in some cases the subject page was one link removed from the actual search form.
Criteria were developed to predict when this might be the case. The BrightPlanet technology was used to retrieve the complete pages and fully index them for both the initial unique sources and the one-link removed sources. A total of 43, resulting URLs were actually retrieved. We then applied a filter criteria to these sites to determine if they were indeed search sites. This proprietary filter involved inspecting the HTML content of the pages, plus analysis of page text content.
tiecapmibooks.ga This brought the total pool of deep Web candidates down to 17, URLs. Subsequent hand inspection of random sites from this listing identified further filter criteria. Ninety-five of these , or This correction has been applied to the entire candidate pool and the results presented. Additionally, automated means for discovering further search sites has been incorporated into our internal version of the technology based on what we learned. The basic technique for estimating total deep Web sites uses "overlap" analysis, the accepted technique chosen for two of the more prominent surface Web size analyses.
Overlap analysis involves pairwise comparisons of the number of listings individually within two sources, n a and n b , and the degree of shared listings or overlap, n 0 , between them. Assuming random listings for both n a and n b , the total size of the population, N, can be estimated.
These pairwise estimates are repeated for all of the individual sources used in the analysis. To illustrate this technique, assume, for example, we know our total population is Then if two sources, A and B, each contain 50 items, we could predict on average that 25 of those items would be shared by the two sources and 25 items would not be listed by either. There are two keys to overlap analysis.