Web harvesting
| It's hard to argue with the proposition that the World Wide Web is the largest repository of information that has ever existed. In just over a decade, the Web has moved from a university curiosity to a fundamental research, marketing and communications vehicle that impinges upon the everyday life of most people in the developed world. But there's a catch, of course. As the amount of information on the Web grows, that information becomes ever harder to keep track of and use. This vast amount of freely available information is spread over billions of Web pages, each with its own independent structure and format. So how do you find the information you're looking for in a useful format -- and do it quickly and easily without breaking the bank? Search Isn't Enough Search engines are a big help, but they can do only part of the work, and they are hard-pressed to keep up with daily changes. For all the power of Google and its kin, all that search engines can do is locate information and point to it. They go only two or three levels deep into a Web site to find information and then return URLs. They also find and return meta descriptions and meta keywords embedded in Web pages, but these may well be inaccurate. Consider that even when you use a search engine to locate data, you still have to do the following tasks to capture the information you need: A better solution, especially for companies that are aiming to exploit a broad swath of data about markets or competitors, lies with Web harvesting tools. Web harvesting software automatically extracts information from the Web and picks up where search engines leave off, doing the work the search engine can't. Extraction tools automate the reading, copying and pasting necessary to collect information for analysis, and they have proved useful for pulling together information on competitors, prices and financial data of all types. Harvesting Techniques There are three ways we can extract more useful information from the Web. The first technique, Web content harvesting, is concerned directly with the specific content of documents or their descriptions, such as HTML files, images or e-mail messages. Since most text documents are relatively unstructured (at least as far as machine interpretation is concerned), one common approach is to exploit what's already known about the general structure of documents and map this to some data model. Another approach to Web content harvesting involves trying to improve on the content searches that tools like search engines perform. This type of content harvesting goes beyond keyword extraction and the production of simple statistics relating to words and phrases in documents. Another technique, Web structure harvesting, takes advantage of the fact that Web pages can reveal more information than just their obvious content. Links from other sources that point to a particular Web page indicate the popularity of that page, while links within a Web page that point to other resources may indicate the richness or variety of topics covered in that page. This is like analyzing bibliographical citations -- a paper that's often cited in bibliographies and other papers is usually considered to be important. The third technique, Web usage harvesting, uses data recorded by Web servers about user interactions to help understand user behavior and evaluate the effectiveness of the Web structure. General access-pattern tracking analyzes Web logs to understand access patterns and trends in order to identify structural issues and resource groupings. Customized usage tracking analyzes individual trends so that Web sites can be personalized to specific users. Over time, based on access patterns, a site can be dynamically customized for a user in terms of the information displayed, the depth of the site structure and the format of the resources presented. Also Known As . . . Over the past decade, the terminology used to describe Web harvesting has undergone several changes. In 1996, researcher Oren Etzioni wrote a paper called "The World Wide Web: Quagmire or Gold Mine?" which was published in the journal Communications of the ACM. Etzioni defined Web mining as the use of data mining techniques to automatically discover and extract information from Web documents and services. In the late 1990s, Richard Hackathorn coined the term Web farming to describe a discipline combining aspects of data warehousing, Web data mining and knowledge-base creation. Around the turn of the millennium, Web harvesting began to replace Web mining as the fashionable buzzphrase, although it can mean different things to different people. Web harvesting can be synonymous with Web mining, Web farming and Web scraping, but it can have other meanings as well. One widespread usage of the term refers specifically to the searching of Web pages for e-mail addresses for resale and use in commercial solicitations (i.e. spam). The Web site of the Medical University of South Carolina defines Web harvesting as "the process of downloading RSS feeds and consolidating them for display." Another related term is Web scraping, an obvious derivation from the 1980s catchphrase "screen scraping," where PC- or mini-based applications accessing mainframe systems emulated 3270 or VT100 terminals. Such applications were quick and cheap but not always reliable. Similarly, Web scraping applications process a Web page's HTML to extract meaningful data, often from live data feeds or by manipulating specific applications. Web scrapers are also cheap and useful but of questionable reliability. Kay is a Computerworld contributing writer in Worcester, Mass. Contact him at russkay@charter.net. | |||||
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