- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full 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).
Contents
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
