What is Data Mining?
Data mining methodologies were hugely adopted in various business domains,
such as database marketing, credit scoring, fraud detection, to name only a few of the areas
where data mining has become an indispensable tool for business success. Increasingly data
mining methods are also being used in industrial process optimization and control. While the
typical approach is similar regardless of application, some specific methodologies and techniques for optimizing continuous processes, like
boiler performance in a coal-burning power plant, have proven particularly useful for those
applications, and superior to existing traditional analytic approaches such as DOE (design of
experiments), CFD (computational fluid dynamics), or statistical modeling.
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The term ’Big Data’ appeared for first time in 1998 in a Silicon Graphics (SGI) slide deck by John Mashey with the title of ”Big Data and the Next Wave of InfraStress” . Big Data mining was very relevant from the beginning, as the first book mentioning ’Big Data’ is a data mining book that appeared also in 1998 by Weiss and Indrukya  . However, the first academic paper with the words ’Big Data’ in the title appeared a bit later in 2000 in a paper by Diebold . The origin of the term ’Big Data’ is due to the fact that we are creating a huge amount of data every day. Each day Google has more than 1 billion queries per day, Twitter has more than 250 milion tweets per day, Facebook has more than 800 million updates per day, and YouTube has more than 4 billion views per day. The data produced nowadays is estimated in the order of zettabytes, and it is growing around 40% every year.
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Say you wanted to optimize a cyclone furnace (an older-type design for burning coal, still in use in many power plants) for stable high flame temperatures. Stable temperatures are necessary to ensure cleaner combustion, and less build-up of undesirable slag that may interfere with heat transfer. Typically, power plants are equipped with very effective data gathering and storage technologies, so there are easy ways to extract the data that describe various parameter settings, as well as flame temperatures, on a minute-by-minute interval. Traditional methods to approach this task – to optimize combustion to achieve stable flame temperatures in the presence of different loads, fuel quality, and so on – come down to the application of a-priori (CFD) models, or more or less trial-and-error parametric testing.
Data mining outperforms rules-based systems for detecting fraud, even as fraudsters become more sophisticated in their tactics. “Models can be built to cross-reference data from a variety of sources, correlating nonobvious variables with known fraudulent traits to identify new patterns of fraud,” Patel said. For its potential to yield predictive insights from masses of diverse data points, data mining has proven to be an invaluable component of any analytics initiative.
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