TY  - JOUR
T1  - Divergence of Managing Scalability and Unstructured Data in Big Data Analytics
AU - Viji, D. AU - Lavanya, R. AU - Hemavathi, D. AU - Saranya, P. 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 9
SP  - 2575
EP  - 2579
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.2575.2579
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.2575.2579
KW  - Big data
KW  -heterogeneity
KW  -scalability
KW  -complexity
KW  -analytics
KW  -heterogeneity
AB  - The promise of data-driven decision-making is now being recognized broadly and there is growing
enthusiasm for the notion of "Big data. Heterogeneity, scale, timeliness, complexity and privacy problems with
big data hinder advancement at all stages of the channel that can form assessment from information. The
promising visualization of big data is to facilitate organizations will be capable to produce and tie together every
byte of related information and use it to make the preeminent decisions. Big data technologies not only support
the ability to collect large amounts but more importantly, the ability to understand and take advantage of its
full value. Traditional data mining techniques are deals with structured, homogeneous and small dataset.But
today =s perspective major characteristics of big data are heterogeneity. In big data mining heterogeneity data
set have to accept and deal with following types of data like structured, semi structured even though, fully
unstructured data simultaneously. In this study, an interesting idea given about partitioning to handle the
heterogeneity data. First it helps to determine whether the given dataset is fully heterogeneity or not. Then the
given dataset is accordingly partitioned into several homogenous subsets. Finally, a specialized model for each
subset is constructed and narrated with various features.
ER  -