Authors :
Dr. Narasimha Chary CH; B. Navya; Dr. Srihari Chintha; Kethavath Nagu
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://t.ly/jWQl
DOI :
https://doi.org/10.5281/zenodo.8012102
Abstract :
Big data analysis challenges include capturing
data, data storage, data analysis,
search, sharing, transfer, visualization, querying,
updating, information privacy, and data source. Big
data was originally associated with three key
concepts: volume, variety, and velocity.[4] The analysis
of big data presents challenges in sampling, and thus
previously allowing for only observations and sampling.
Thus a fourth concept, veracity, refers to the quality or
insightfulness of the data. Without sufficient investment
in expertise for big data veracity, then the volume and
variety of data can produce costs and risks that exceed
an organization's capacity to create and
capture value from big data.
Keywords :
Monumental data, monumental data analytics, Healthcare, Public health.
Big data analysis challenges include capturing
data, data storage, data analysis,
search, sharing, transfer, visualization, querying,
updating, information privacy, and data source. Big
data was originally associated with three key
concepts: volume, variety, and velocity.[4] The analysis
of big data presents challenges in sampling, and thus
previously allowing for only observations and sampling.
Thus a fourth concept, veracity, refers to the quality or
insightfulness of the data. Without sufficient investment
in expertise for big data veracity, then the volume and
variety of data can produce costs and risks that exceed
an organization's capacity to create and
capture value from big data.
Keywords :
Monumental data, monumental data analytics, Healthcare, Public health.
