A Different Approach to the Meaning and Mining of Small Data

Ryan H Rindlisbacher^*

Computer Science Student at North Central University, Prescot, Arizona, USA

*Corresponding Author : Ryan H Rindlisbacher
Computer Science Student at Northcentral University, Prescot, Arizona, USA
Tel: +1 801-232-3790
E-mail: r.rindlisbacher0165@o365.ncu.edu

Received: June 26, 2018 Accepted: July 17, 2018 Published: July 23, 2018

Citation: Rindlisbacher RH (2018) A Different Approach to the Meaning and Mining of Small Data. J Comput Eng Inf Technol 7:3. doi: 10.4172/2324-9307.1000201

Big data is rage in data mining but is typically limited to only big businesses. Small data is generally thought of as a subset of big data. For example, Google holds trillions of records used for searching, but this is not useful in and of itself. When one searches something on Google, typically only ten to fifteen results are shown on the first page. In order for this to occur, Google has taken big data and produced small data. The true value is in the small data subsets. This idea, however, that small data is the subset of big data, is not the only definition of small data. Small businesses, that only produce small data, can also benefit from data mining practices. What issues arise when only looking at small data?