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.

• 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