Big Data Research Productivity A Scientometric Study

Abstract

Research is a scientific and technological growth, increasing productivity in daily newlineactivities, and improving human life. All the scholarly research scientific works end up as newlinepublications are usually contained in a report, articles, monographs, etc. The newlinescientometrics discipline has emerged to constantly monitor these publications and their newlineassociated bibliographic parameters. It studies the development of science through the newlinestatistical processing of many measurements and bibliographic information. (The number newlineof scholarly articles, their citation, the significance of the journals in which they are newlinepublished, etc.) Using scientific data, one can determine the academic potential of the newlineindividual scholar, a research team, a university, and even an entire country with varying newlinedegrees of accuracy. Bibliometric, Scientometric, Informetrics, Mapping of subjects, newlinequantitative analysis of publication, and citation analysis, have become a standard tools for newlineresearch evaluation. newlineThe term Scientometrics is a field that applies quantitative methods to the study of science newlineas an information process. It is the science of measuring the quality of science. The newlinechapter two will covers all concepts related to scientometrics and Big Data and their newlineorigin. Thus Scientometrics involves studies like Sociology of science, History of science, newlineGrowth of science and scientific institutions, Behavior of science and scientists, Science newlinepolicy and decision-making. newlineThe study adopted the three bibliometrics laws such as Bradford s Law of Scattering; newlineLotka s Law of Scientific Productivity (Inverse Square Law); and Zipf s Law of Word newlineOccurrence. newlineThe characteristics of Big Data are explained with the enumeration of volume, variety, newlinevelocity, variability, veracity, viscosity, value, visualization, volatility with challenges of newlineBig Data. Huge data with massive growth, Generating vision from Big Data, integrating newlinedata from different sources, data validation some challenges. newlineToday, consumers are very smart before buying any product consumer checks