Streaming Analytics
Concepts, architectures, platforms, use cases and applications
Published on 4. November 2022
Book
Hardback
468 pages
978-1-83953-416-4 (ISBN)
Description
When digitized entities, connected devices and microservices interact purposefully, we end up with a massive amount of multi-structured streaming (real-time) data that is continuously generated by different sources at high speed. Streaming analytics allows the management, monitoring, and real-time analytics of live streaming data. The topic has grown in importance due to the emergence of online analytics and edge and IoT platforms. A real digital transformation is being achieved across industry verticals through meticulous data collection, cleansing and crunching in real time. Capturing and subjecting those value-adding events is considered to be the prime task for achieving trustworthy and timely insights.
The authors articulate and accentuate the challenges widely associated with streaming data and analytics, describe data analytics algorithms and approaches, present edge and fog computing concepts and technologies and show how streaming analytics can be accomplished in edge device clouds. They also delineate several industry use cases across cloud system operations in transportation and cyber security and other business domains.
The book will be of interest to ICTs industry and academic researchers, scientists and engineers as well as lecturers and advanced students in the fields of data science, cloud/fog/edge architecture, internet of things and artificial intelligence and related fields of applications. It will also be useful to cloud/edge/fog and IoT architects, analytics professionals, IT operations teams and site reliability engineers (SREs).
The authors articulate and accentuate the challenges widely associated with streaming data and analytics, describe data analytics algorithms and approaches, present edge and fog computing concepts and technologies and show how streaming analytics can be accomplished in edge device clouds. They also delineate several industry use cases across cloud system operations in transportation and cyber security and other business domains.
The book will be of interest to ICTs industry and academic researchers, scientists and engineers as well as lecturers and advanced students in the fields of data science, cloud/fog/edge architecture, internet of things and artificial intelligence and related fields of applications. It will also be useful to cloud/edge/fog and IoT architects, analytics professionals, IT operations teams and site reliability engineers (SREs).
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 239 mm
Width: 160 mm
Thickness: 28 mm
Weight
930 gr
ISBN-13
978-1-83953-416-4 (9781839534164)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Pethuru Raj is the Chief Architect and Vice President in the Site Reliability Engineering (SRE) division of Reliance Jio Platforms Ltd., Bangalore, India. He focuses on emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), big and fast data analytics, blockchain, digital twins, cloud-native computing, edge & fog clouds, reliability engineering, microservices architecture (MSA), and event-driven architecture (EDA). He has authored and edited 20 technology books. He holds a CSIR-sponsored PhD from Anna University, India.
Chellammal Surianarayanan is an assistant professor of computer science at Government Arts and Science College, Tiruchirapalli, India. Her contributions include the development of an embedded system for lead shield integrity assessment system, portable automatic air sampling equipment, an embedded system of detection of lymphatic filariasis in its early stage, and the development of data logging software applications for atmospheric dispersion studies. She is a life member of the Computer Society of India and IAENG. She earned a Doctorate in Computer Science by developing computational optimization models for the discovery and selection of semantic services.
Koteeswaran Seerangan is an associate professor in the Department of Computer Science and Engineering, School of Computing, and Dean of Research Studies at Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, India. His research interests include big data and analytics, Internet of Things, and machine learning. He is a member of ACM and the IET, and a senior member of IEEE and life member of ISTE. He holds a PhD in computer science and engineering from Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, India.
George Ghinea is a professor in multimedia computing in the Department of Computer Science at Brunel University, London, UK. His research activities lie at the confluence of computer science, media and psychology. His work focuses on the area of perceptual multimedia quality and how one builds end-to-end communication systems incorporating user perceptual requirements. He has applied his expertise in areas such as eye-tracking, telemedicine, multi-modal interaction, and ubiquitous and mobile computing. He consults regularly for both public and private institutions. He is a member of the IEEE. He holds a PhD in computer science from the University of Reading, UK.
Chellammal Surianarayanan is an assistant professor of computer science at Government Arts and Science College, Tiruchirapalli, India. Her contributions include the development of an embedded system for lead shield integrity assessment system, portable automatic air sampling equipment, an embedded system of detection of lymphatic filariasis in its early stage, and the development of data logging software applications for atmospheric dispersion studies. She is a life member of the Computer Society of India and IAENG. She earned a Doctorate in Computer Science by developing computational optimization models for the discovery and selection of semantic services.
Koteeswaran Seerangan is an associate professor in the Department of Computer Science and Engineering, School of Computing, and Dean of Research Studies at Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, India. His research interests include big data and analytics, Internet of Things, and machine learning. He is a member of ACM and the IET, and a senior member of IEEE and life member of ISTE. He holds a PhD in computer science and engineering from Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, India.
George Ghinea is a professor in multimedia computing in the Department of Computer Science at Brunel University, London, UK. His research activities lie at the confluence of computer science, media and psychology. His work focuses on the area of perceptual multimedia quality and how one builds end-to-end communication systems incorporating user perceptual requirements. He has applied his expertise in areas such as eye-tracking, telemedicine, multi-modal interaction, and ubiquitous and mobile computing. He consults regularly for both public and private institutions. He is a member of the IEEE. He holds a PhD in computer science from the University of Reading, UK.
Editor
Chief Architect and Vice-PresidentReliance Jio Platforms Ltd., Site Reliability Engineering (SRE) Division, Bangalore, India
Assistant ProfessorGovernment Arts and Science College, Tiruchirapalli, India
Associate ProfessorVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Department of Computer Science and Engineering, School of Computing, India
ProfessorBrunel University, Department of Computer Science, London, UK
Content
Chapter 1: Streaming data processing - an introduction
Chapter 2: Event processing platforms and streaming databases for event-driven enterprises
Chapter 3: A survey on supervised and unsupervised algorithmic techniques to handle streaming Big Data
Chapter 4: Sentiment analysis on streaming data using parallel computing
Chapter 5: Fog and edge computing paradigms for emergency vehicle movement in smart city
Chapter 6: Real-time stream processing on IoT data for real-world use cases
Chapter 7: Rapid response system for road accidents using streaming sensor data analytics
Chapter 8: Applying streaming analytics methods on edge and fog device clusters
Chapter 9: Delineating IoT streaming analytics
Chapter 10: Describing the IoT data analytics methods and platforms
Chapter 11: Detection of anomaly over streams using isolation forest
Chapter 12: Detection of anomaly over streams using big data technologies
Chapter 13: Scalable and real-time prediction on streaming data - the role of Kafka and streaming frameworks
Chapter 14: Object detection techniques for real-time applications
Chapter 15: EdgeIoTics: leveraging edge cloud computing and IoT for intelligent monitoring of logistics container volumes
Chapter 16: A hybrid streaming analytic model for detection and classification of malware using Artificial Intelligence techniques
Chapter 17: Performing streaming analytics on tweets (text and images) data
Chapter 18: Machine learning (ML) on the Internet of Things (IoT) streaming data toward real-time insights
Chapter 2: Event processing platforms and streaming databases for event-driven enterprises
Chapter 3: A survey on supervised and unsupervised algorithmic techniques to handle streaming Big Data
Chapter 4: Sentiment analysis on streaming data using parallel computing
Chapter 5: Fog and edge computing paradigms for emergency vehicle movement in smart city
Chapter 6: Real-time stream processing on IoT data for real-world use cases
Chapter 7: Rapid response system for road accidents using streaming sensor data analytics
Chapter 8: Applying streaming analytics methods on edge and fog device clusters
Chapter 9: Delineating IoT streaming analytics
Chapter 10: Describing the IoT data analytics methods and platforms
Chapter 11: Detection of anomaly over streams using isolation forest
Chapter 12: Detection of anomaly over streams using big data technologies
Chapter 13: Scalable and real-time prediction on streaming data - the role of Kafka and streaming frameworks
Chapter 14: Object detection techniques for real-time applications
Chapter 15: EdgeIoTics: leveraging edge cloud computing and IoT for intelligent monitoring of logistics container volumes
Chapter 16: A hybrid streaming analytic model for detection and classification of malware using Artificial Intelligence techniques
Chapter 17: Performing streaming analytics on tweets (text and images) data
Chapter 18: Machine learning (ML) on the Internet of Things (IoT) streaming data toward real-time insights