Various Algorithms & Techniques Driving Data Science for Big Data
JSVG Krishna1, M. Venkateswara Rao2, Kattupalli Sudhakar3
1Prof. JSVG Krishna*, Associate Professor, Department of CSE, SIR CRR Engineering College, Eluru, India.
2Dr. M. Venkateswara Rao, Professor, Department of CSE, GITM University, Visakhapatnam, India. E-mail:
3Prof. Kattupalli Sudhakar, Associate Professor, Department of CSE, PSCMR College of Engineering and Technology, Vijayawada, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on March 01, 2020. | Manuscript published on March 10, 2020. | PP: 425-727 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2626039520/2020©BEIESP | DOI: 10.35940/ijitee.E2626.039520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In basic terms, Big Data1 – when joined with Data Science2 – permit chiefs to gauge and survey fundamentally more data about the nuances of their organizations, and to utilize the data in settling on progressively keen choices. In early 2010, during the period when the development of Big Data was truly increasing noteworthy notification all through the 3Data Management industry, said that it “is advancing into the key reason for rivalry.” It has now developed, information volumes proceed to develop, and now the inquiry is never again if it’s another pattern and what influences it will have, yet how to use Big Data in significant manners for the venture. Information Science has been around for any longer than Big Data, yet it wasn’t until the development of information volumes arrived at contemporary levels that Data Science has become an essential part of big business level Data Management.
Keywords: About four Key Words or Phrases in Alphabetical Order, Separated by Commas
Scope of the Article: Parallel and Distributed Algorithms