A Review on Big Data Applications and their Challenges
Abstract
Understanding huge amounts of data with a wide variety of data kinds is referred to as big data analytics. “Human, Machine and Material” development strategy will result in an enormous amount of data. The management department may enhance its potential to process big data by assessing and analysing current network big data issues. As a consequence, it considerably plays a role in minimising resource costs and consumption in every sector. Every sector can effortlessly transition into the following information and digitalisation phase of development. Big data will aid in tackling challenges and enhancing knowledge across various sectors. Although, the efficiency of big data analytics is still questioned by some challenges. The challenges that arise in big data analytics are storage, data quality, lack of data science professionals, data accumulation and data validation. Therefore, this discusses the term “Big data analytics” by configuring its applications, tools, Machine Learning (ML) models and challenges in existing approaches. A comprehensive analysis of over 58 research papers, covering various aspects of big data analytics across multiple domains including healthcare, education, agriculture, multimedia and travel is presented in this study. The main objective of this survey is to contribute to advancing knowledge, facilitating informed decision-making and guiding future research efforts in the dynamic and rapidly evolving landscape of big data analytics. Through meticulous paper selection, a diverse representation of the latest advancements in big data analytics techniques was curated. Each domain underwent a thorough review, elucidating methodologies, tools, datasets and performance measures. Further, the general steps involved in big data analytics techniques are outlined by providing a foundational understanding. Key areas of analysis include chronological review, algorithms utilised, tools and datasets employed and performance evaluation measures. By addressing these aspects, the study offers valuable insights into the evolution, methodologies and performance of big data analytics techniques across diverse domains. Additionally, it identifies research gaps and challenges, paving the way for future research to address critical issues such as data interoperability, privacy concerns and scalability. This study serves as a comprehensive resource for researchers, practitioners and policymakers, contributing to advancing knowledge and facilitating informed decision-making in the rapidly evolving landscape of big data analytics.
