Big data is a collection of large and complex data sets whic h becomes difficult to process using on-hand database management tools or traditional data processing applications. It is becoming very difficult f or organizations to store\, retrieve and process the ever-increasing data. The problem lies in the use of traditional systems to store enormous data .
Big data analytics refers to the process of collecting\, organizin
g and analyzing large sets of data to discover patterns and other useful i
nformation. To analyze a large volume of data\, big data analytics is typi
cally performed using specialized software tools and applications for pred
ictive analytics\, data mining\, text mining\, forecasting and data optimi
zation. Hadoop is an open source software framework that supports the proc
essing of large data sets in a distributed computing environment.
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Bioinformatics is the application of computer technology to the management
of biological information. Bioinformatics is an interdisciplinary field
that develops methods and software tools for understanding biological data
. As an interdisciplinary field of science\, Bioinformatics combines Compu
ter science\, Statistics\, Mathematics and Engineering to study and proces
s biological data. Big data requires significantly more efficient storage
and data management solutions that seem impossible or infeasible to imple
ment even a few years ago. In recent days\, big data presents significant
opportunities in uncovering hidden patterns in data and building predictiv
e models for practical biomedical applications.
\nThe aim of this se
minar is to bring together researchers in database management\, data minin
g\, artificial Intelligence\, computational biology\, Bioinformatics to di
scuss research challenges and cutting edge techniques in handling big biol
ogical data and also it brings together big data practitioners\, researche
rs\, students\, clinicians\, health IT experts and data scientists to shar
e ideas on how to improve the state of our healthcare systems by deliverin
g on the promise of big data infrastructure investments.