A Study Of The Impact Of Big Data On Information Science Research Methods

Abstract

This abstract presents a comprehensive analysis of the impact of big data on research methodologies in information science. The study investigates how the defining characteristics of big data, namely its volume, variety, and velocity, have introduced novel challenges and opportunities for researchers in the field.
The key aspects addressed include:
• The conceptualization and significance of big data in information science research.
• The complexities associated with unstructured big data and the necessity for data refinement and unit definition.
• The ongoing relevance of inferential statistical analysis methods, encompassing sampling techniques and considerations related to p-value manipulation.
• The application of exploratory and statistical methodologies to big data, including correlation and regression analysis, natural language processing, sentiment analysis, temporal analysis, and visualization techniques.
• The utilization of link analysis and clustering methods to identify patterns and relationships within large-scale datasets.
The research elucidates how these methodologies have been adapted and implemented to analyse extensive datasets derived from sources such as social media platforms, search engine logs, and digital libraries. It underscores the importance of comprehending and effectively employing these research methods to address the challenges and capitalize on the opportunities presented by big data in information science research.