Big Data Platforms and Applications : Case Studies, Methods, Techniques, and Performance Evaluation

Gabriel Neagu

ISBN 10: 3030388387 / ISBN 13: 9783030388386

Edité par Springer International Publishing, 2022

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - This book provides a review of advanced topics relating to the theory, research, analysis and implementation in the context of big data platforms and their applications, with a focus on methods, techniques, and performance evaluation. The explosive growth in the volume, speed, and variety of data being produced every day requires a continuous increase in the processing speeds of servers and of entire network infrastructures, as well as new resource management models. This poses significant challenges (and provides striking development opportunities) for data intensive and high-performance computing, i.e., how to efficiently turn extremely large datasets into valuable information and meaningful knowledge.The task of context data management is further complicated by the variety of sources such data derives from, resulting in different data formats, with varying storage, transformation, delivery, and archiving requirements. At the same time rapid responses are neededfor real-time applications. With the emergence of cloud infrastructures, achieving highly scalable data management in such contexts is a critical problem, as the overall application performance is highly dependent on the properties of the data management service.

N° de réf. du vendeur 9783030388386

Détails bibliographiques

• Titre: Big Data Platforms and Applications : Case Studies, Methods, Techniques, and Performance Evaluation
• Éditeur: Springer International Publishing
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 3030388387
• ISBN à 13 chiffres: 9783030388386
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

This book provides a review of advanced topics relating to the theory, research, analysis and implementation in the context of big data platforms and their applications, with a focus on methods, techniques, and performance evaluation.

The explosive growth in the volume, speed, and variety of data being produced every day requires a continuous increase in the processing speeds of servers and of entire network infrastructures, as well as new resource management models. This poses significant challenges (and provides striking development opportunities) for data intensive and high-performance computing, i.e., how to efficiently turn extremely large datasets into valuable information and meaningful knowledge.

The task of context data management is further complicated by the variety of sources such data derives from, resulting in different data formats, with varying storage, transformation, delivery, and archiving requirements. At the same time rapid responses are neededfor real-time applications. With the emergence of cloud infrastructures, achieving highly scalable data management in such contexts is a critical problem, as the overall application performance is highly dependent on the properties of the data management service.

À propos de l?auteur

Dr. Florin Pop is a Professor at the Department of Computer Science and Engineering at the University Politehnica of Bucharest, Romania and a Senior Researcher (1st Degree) at the Department of Intelligent and Distributed Data Intensive Systems at the National Institute for Research and Development in Informatics, Bucharest, Romania.

Dr. Gabriel Neagu is a Senior Researcher (1st Degree) at the Department of Intelligent and Distributed Data Intensive Systems at the National Institute for Research and Development in Informatics, Bucharest, Romania.