Dr. Dobb's Journal - May 2008 - (Page 48)

d05zeid_p3db 3/13/08 9:36 AM Page 48 Instantly Search Terabytes of Text N dozens of indexed, unindexed, fielded data and full-text search options (including Unicode support for hundreds of international languages) N file parsers / converters for hit-highlighted display of all popular file types N Spider supports static and dynamic web data; highlights hits while displaying links, formatting and images intact N API supports .NET, C++, Java, databases, etc. New .NET Spider API h Spider Desktop wit h Spider Network wit CD/DVDs Publish for ider Web with Sp Win & .NET Engine for Linux Engine for New 64-bit The Smart Choice for Text Retrieval ® since 1991 N “Bottom line: dtSearch manages a terabyte of text in a single index and returns results in less than a second” – InfoWorld N “For combing through large amounts of data,” dtSearch “leads the market” – Network Computing N dtSearch “covers all data sources powerful Web-based engines”– eWEEK N dtSearch “searches at blazing speeds” – Computer Reseller News Test Center See www.dtsearch.com for hundreds more reviews, and hundreds of developer case studies Contact dtSearch for fully-functional evaluations 1-800-IT-FINDS www.dtsearch.com 48 Dr. Dobb’s Journal l www.ddj.com l May 2008 wishes to study various aspects of the retrieved information such as statistical distributions of the information. Effectively, these thresholds can be used as low pass, high pass, and band pass filters. An object relationship filter lets users select an object Oi that they feel is characteristic of an object that the user wants to see or that the user does not want to see. All similar objects are then removed, or all dissimilar objects are removed, depending on whether the filter is a positive object relationship filter or a negative object relationship filter. For a positive object relationship filter, the user selects an object Oi and specifies a minimum relationship value RM. Object Oi and all objects Ok such that the relationship Rik between objects Oi and Ok is greater than or equal to the minimum relationship value RM are eliminated from the user information domain DU. In this case, object Oi is selected as an example of an object that the user feels is not relevant. In the case of a search engine, the user would select a web page that the user feels is not relevant. That web page and all similar web pages would be eliminated from the search results. For a negative object relationship filter, the user selects an object Oi and specifies a minimum relationship value RM. All objects Ok such that the relationship Rik between objects Oi and Ok is less than the minimum relationship value RM are eliminated from the user information domain DU. In this case, object Oi is selected as an example of an object that the user feels is particularly relevant. In the case of a search engine, the user would select a web page that the user feels is most relevant, and all dissimilar web pages would be eliminated from the search results. Object-relationship filtering lets users select objects to be included/excluded from the results without understanding the details of why the object is relevant or is not relevant. This can be very important because unsophisticated users can look at the results of a web search and recognize when they find good results and bad results, but may not define those good and bad results in terms of keywords. Objectrelationship filtering has great potential for e-commerce. Often consumers may not be able to easily create keywords to define the items they desire. But they know it when they see it. They can click on an exemplary item from a search and obtain a list of all similar items. Conclusion Better methods of information retrieval will always be needed and these methods are improving regularly. Better methods of information display are also important and there is a great demand for it as evidenced by the success of Google, one of whose major innovations was in the area of information display. Automatic filtering of retrieved information is a great goal and research is going on in that area also. However, automatic filtering may never be 100-percent accurate and human-aided filtering has many great benefits that have yet to be fully exploited. References Nicholas J. Belkin and W. Bruce Croft, “Information Filtering and Information Retrieval: Two Sides of the Same Coin?” Communications of the ACM, 35(12), 2938, 1992. Sergey Brin and Lawrence Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” WWW7/Computer Networks 30(1-7): 107-117, 1998. Peter W. Foltz and Susan T. Dumais, “Personalized Information Delivery: An Analysis of Information Filtering Methods,” Communications of the ACM, 35(12), 5160, 1992. Lee Gomes, “Computer Scientists Pull a Tom Sawyer To Finish Grunt Work,” The Wall Street Journal, June 27, 2007. Peter Pirolli and Stuart K. Card, “Information foraging,” Psychological Review, 106: 643-675, 1999. G. Salton and M.J. McGill, Introduction to Modern Information Retrieval, McGraw Hill, New York, 1983. Bob Zeidman, “Detecting Source-Code Plagiarism,” Dr. Dobb’s Journal, July 2004, 55-60. Bob Zeidman, “Iterative Filtering of Retrieved Information to Increase Relevance,” The 11th World Multi-Conference on Systemics, Cybernetics and Informatics, July 11, 2007. DDJ http://www.dtsearch.com http://www.dtsearch.com http://www.ddj.com

Table of Contents Feed for the Digital Edition of Dr. Dobb's Journal - May 2008

Dr. Dobb's Journal - May 2008 Contents Friday Night Fish Fry Alia Vox Developer Diaries Software Development Goes to the Movies Cat: A Functional Stack-Based Little Language Mojax: Mobile Ajax Framework Kernel-Mode Databases Getting Better Search Results Effective Concurrency The Agile Edge

Dr. Dobb's Journal - May 2008

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