Big data is data that has been defined as having all of the characteristics defined by the “5 Vs”: volume (lots of data), variety (highly diverse data), velocity (changing very fast), veracity (hard to fully validate) and Value. Data Mining is an exploratory data-analytic process that detects interesting, novel patterns within one or more data sets (that are usually large). It employs a variety of techniques, including the machine-learning techniques and standard multivariate statistical techniques.
Aims:
Big data is data that has been defined as having all of the characteristics defined by the “5 Vs”: volume (lots of data), variety (highly diverse data), velocity (changing very fast), veracity (hard to fully validate) and Value. Data Mining is an exploratory data-analytic process that detects interesting, novel patterns within one or more data sets (that are usually large). It employs a variety of techniques, including the machine-learning techniques and standard multivariate statistical techniques.
Methods and applications:
Big data is always defined by its 5-v characteristics which are Volume, Velocity, Veracity, Variety, and Value. Almost each data model comprising big data is dependent on these 5-v characteristics. A large number of researches have been done on velocity and volume, but the complete and efficient solution for the variety is still not available in the markets. Data mining research aims to analyse huge data sets in order to get insights and find patterns in data is called big data analytics. Big data analytics is the need of every corporate and state of the art organization to look forward and make useful decisions. This is very true concerning healthcare system.
Open source research focus on some applications will:
Detect complex patterns in chronic diseases.
Improve the organisation of data so that it can easily be rendered and analysed.
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