Название: New Approaches to Data Analytics and Internet of Things Through Digital TwinАвтор: P. Karthikeyan, Polinpapilinho F. Katina, S.P. Anandaraj
Издательство: IGI Global
Год: 2023
Страниц: 326
Язык: английский
Формат: pdf (true), epub
Размер: 29.5 MB
Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the Internet of Things (IoT) in today's modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the Internet of Things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, Deep Learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.
Data Analytics is the process of analyzing the raw data in order to draw inferences about the information at hand. Data analysis techniques are primarily used to get an insight which further facilitates the enhancement of the sector under consideration. These techniques are beneficial for optimizing a process under consideration and increasing a system’s overall efficiency. These techniques also act as performance boosters as their implementation in the business models helps in the reduction of costs by a considerable amount. It is the most important for any organization as it facilitates a better decision-making approach and provides an analysis of customer trends and satisfaction, which further leads to novice and improved products and services. It also helps in the effective marketing of the products and services. Data analytics has widespread applications in various sectors. Various tools are used for carrying out data analytics jobs.
Big Data Analytics (BDA) is the computational branch of study that aims to find patterns, trends, and associations in Big Data. It primarily involves the application of the operators such as Gathering, Selection, Pre-processing, Transformation, Mining, Evaluation, and Interpretation for exploring raw data. Key technologies such as KDD (Data Mining), AI and Machine Learning have been applied to analyse the raw data for discovering the hidden gems of novel, interesting, actionable knowledge. A few of the popular software used in the field of Data Analytics are R programming, Tableau, Python programming, SAS, Apache Spark, and Excel etc. In addition, various statistical, Machine Learning and Artificial Intelligence algorithms have been used for analytics purposes for a long time. But with the emergence of age of Big Data, the traditional Data Analytics may not be able to handle such enormous quantities of data.
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