Название: Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions
Автор: Sarvesh Pandey, Udai Shanker, Vijayalakshmi Saravanan, Rajinikumar Ramalingam
Издательство: Springer
Год: 2023
Страниц: 339
Язык: английский
Формат: pdf (true), epub
Размер: 38.7 MB

Data analytics and Machine Learning technologies, particularly in a decentralized scenario, are offering cost-effective solutions for many real-life problems. Recent developments in computer technology have led to increased research interests in the field of modern data-intensive distributed computing systems.

This book discusses the application of data systems and data-driven infrastructure in existing industrial systems in order to optimize workflow, utilize hidden potential, and make existing systems free from vulnerabilities. The book discusses application of data in the health sector, public transportation, the financial institutions, etc. Topics include real-time applications in the current Big Data perspective; improving security in IoT devices; data backup techniques for systems; artificial intelligence-based outlier prediction; Machine Learning in OpenFlow Network; and application of Deep Learning in blockchain enabled applications. This book is intended for a variety of readers from professional industries, organizations, and students.

Chapter 1 talks about energy-conscious scheduling of resources for fault-tolerant distributed computing systems. This chapter emphasizes the point that reliability should be given equal weightage as that to deadline aspect of such system design. Chapter 2 discusses how advanced morphological component analysis and steganography could be utilized for the purpose of secret data transmission. Chapter 3 puts light on cyber-security aspects of data management in wireless sensor networks. Chapter 4 proposes a dynamic privacy protection scheme for trajectory data.

Chapter 5 proposes an idea of how integration of mobile agent systems with e-governance can lead to better/transparent and dynamic infrastructure with no loss to reliability and fault tolerance. Chapter 6 not only discusses this problem but also attempts to resolve this issue by using some of the existing Machine Learning techniques. In Chap. 7, using Machine Learning classifiers, authors attempted to predict device-specific information from picture data.

Dependence on vehicles has increased manifold in the twentieth century. Now, with advent of the Internet, researchers started working on the idea of “Internet of Vehicles (IoV).” After that, a cross-injection of IoV and blockchain technology has continued to be a research area with lots of potential. Chapter 8 puts light on all these aspects. Traditional bidding system can also benefit from blockchain technology. Chapter 9 discusses this. With integration of blockchain, without any doubt, transparency of bidding process would increase. Chapter 10 talks about vehicular ad hoc networks (VANETs). Various security challenges one may face with VANET-based systems are nicely discussed in this chapter.