Article Preview
Top2. Big Data
Big Data is present in all areas and sectors worldwide. However, it's complexity exceeds the processing power of traditional tools, requiring high-performance computing platforms to exploit the full power of Big Data (Shim, 2013). These requirements have undoubtedly become a real challenge. Many studies focus on the search of methodologies that allow lowering computational costs with an increase in the relevance of extracted information. The need to extract useful knowledge has required researchers to apply different machine learning techniques, to compare the results obtained and to analyze them according to the characteristics of the large data volumes (volume, velocity, veracity and variety, the 4V's) (Mujeeb & Naidu, 2015).
The techniques used by Machine Learning (ML) are focused on minimizing the effects of noise from digital images, videos, hyperspectral data, among others, extracting useful information in various areas of knowledge, such as civil engineering (Rashidi, 2016), medicine (Athinarayanan, 2016), remote Sensing (Torralba, 2008).
With the various repositories of images that have been generated over the last years, many computer vision algorithms try to solve problems related to finding matches for existing local image features in Big Data, grouping the characteristics and labeling them (Muja, 2009).