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For complete information about installing full-text search, see the SQL Server 2005 Books Online article Installing and Upgrading Full-Text Search. SQL Server 2005 Books Online is installed as part of SQL Server 2005. Updates for SQL Server 2005 Books Online are available for download at www.microsoft.com/technet/prodtechnol/sql/2005/downloads/books.mspx.
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Michael Hotek One of the largest recruiting agencies in the world spent years developing a proprietary application that allowed recruiters to quickly and flexibly search the agency s database for resumes that matched desired criteria. On any given day, agency employees ran thousands of queries against several hundred thousand resumes to fill thousands of openings spanning every industry and job function. To its competitors, this company was the model of success. However, this success came at the cost of hundreds of hard-working research assistants who spent 35 40 hours a week parsing resumes into a massive keyword index because the programming team couldn t keep pace with the industry s rate of change. Every week, the recruiting agency had to deal with hundreds of new job titles, technology changes, and terminology shifts. The IT team loaded all these changes into the automated parsing routines on which the search system was based. Then the team executed hundreds of tests to ensure accurate results before releasing the new code base. After the new search code was released, the IT team had to reparse the entire database of resumes, compare it with the previous parsing, and then rebuild the keyword index. When the system was originally deployed, this process took two to three days. Two years later, it was taking four to five weeks and growing longer all the time. The company had to find a solution.
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I was called in to help, and after spending about three hours gaining an understanding of the company s environment, I asked the IT staff if we could run a simple set of tests on a prototype solution. The staff was hesitant because all previous tests they had performed with a variety of vendors required days or weeks of effort and yielded mixed results. But after assuring them that the initial tests should be completed by the end of the day, I was able to proceed. We installed SQL Server 2000 s Full-Text Search component, built a full-text catalog, and added two indexes. The entire process took about an hour on the subset of test data we were using. We then executed hundreds of the IT team s test queries and compared the results with previous results. The results weren t encouraging. Less than 10 percent of the results from the full-text queries matched the results from the proprietary search algorithms. We then looked at the results more closely. It turns out that our full-text queries were picking up thousands of resumes that the proprietary algorithms missed due to misspellings, synonyms, and other factors. The full-text results were also more accurate when dealing with the series of keywords on which recruiters normally searched. Our simple test turned into a full-blown pilot program. In less than a day, the developers could switch over the application s querying capability to use the full-text index. Three days later, the application was in production with spectacular results. The day the new application went into production, the company shattered all previous records for matching potential candidates to job openings. Over the next two months, the company hit a record for placements, only to break it the following week. The agency no longer needed the position of research assistant, so it moved its research assistants into other roles, with most of them receiving promotions to junior recruiter. Implementing the full-text feature also let the company eliminate the entire scanning and optical character recognition (OCR) process it previously used. Resumes submitted in plain-text format were loaded into one column. Resumes that were submitted in any other format were converted to Microsoft Word or PDF format and loaded directly into the database. The IT team then used the full-text engine with add-in filters that could break the resumes down into words and index them in native document format without requiring any of the previous time-consuming text conversions.
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