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In-database processing, sometimes referred to as in-database analytics, refers to the integration of data analytics into data warehousing functionality. Today, many large databases, such as those used for credit card fraud detection and investment bank risk management, use this technology because it provides significant performance improvements over traditional methods. ==History== Traditional approaches to data analysis require data to be moved out of the database into a separate analytics environment for processing, and then back to the database. (SPSS from IBM are examples of tools that still do this today). Doing the analysis in the database, where the data resides, eliminates the costs, time and security issues associated with the old approach by doing the processing in the data warehouse itself. Though in-database capabilities were first commercially offered in the mid-1990s, as object-related database systems from vendors including IBM, Illustra/Informix (now IBM) and Oracle, the technology did not begin to catch on until the mid-2000s. The concept of migrating analytics from the analytical workstation and into the Enterprise Data Warehouse was first introduced by Thomas Tileston in his presentation entitled, “Have Your Cake & Eat It Too! Accelerate Data Mining Combining SAS & Teradata” at the Teradata Partners 2005 "Experience the Possibilities" conference in Orlando, FL, September 18–22, 2005. Mr. Tileston later presented this technique globally in 2006,〔http://www.itworldcanada.com/article/business-intelligence-taking-the-sting-out-of-forecasting/7193〕 2007〔http://www2.sas.com/proceedings/forum2007/371-2007.pdf〕〔http://de.saswiki.org/wiki/SAS_Global_Forum_2007〕〔http://lexjansen.com/cgi-bin/sug_proceedings_pdf.php?c=SUGI&x=SGF2007〕 and 2008.〔http://www.teradata.kr/teradatauniverse/PDF/Track_2/2_2_Warner_Home_Thomas_Tileston.pdf〕 At that point, the need for in-database processing had become more pressing as the amount of data available to collect and analyze continues to grow exponentially (due largely to the rise of the Internet), from megabytes to gigabytes, terabytes and petabytes. This “big data” is one of the primary reasons it has become important to collect, process and analyze data efficiently and accurately. Also, the speed of business has accelerated to the point where a performance gain of nanoseconds can make a difference in some industries.〔 Additionally, as more people and industries use data to answer important questions, the questions they ask become more complex, demanding more sophisticated tools and more precise results. All of these factors in combination have created the need for in-database processing. The introduction of the column-oriented database, specifically designed for analytics, data warehousing and reporting, has helped make the technology possible. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「In-database processing」の詳細全文を読む スポンサード リンク
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