In recent decades, data aggregation and correlation have emerged as a significant and
challenging area of research for finding useful relationships between different features of …

Data is a precious resource in today's society, and it is generated at an unprecedented and
constantly growing pace. The need to store, analyze, and make data promptly available to a …

This chapter provides students, industry experts, and researchers a high-level and
comprehensive overview of the end-to-end architectures of big data stream processing …

Data Stream Processing (DSP) applications analyze data flows in near real-time by means
of operators, which process and transform incoming data. Operators handle high data rates …

Dynamic streaming data is widespread in various real-world scenarios, and the distribution
may change under unforeseen disturbances. The decrease in predicted performance …

Sensory data profoundly influences the quality of detected events in a distributed complex
event processing system (DCEP). Since each sensor's status is unstable at runtime, a single …

The emergence of the Digital Twin of Earth (DTE) signifies a pivotal advancement in the field
of the Digital Earth, which includes observations, simulations, and predictions regarding the …

Today's IoT applications exploit the capabilities of three different computation environments:
sensors, edge, and cloud. Ensuring fault tolerance at the edge level presents unique …

One of the most important issues in distributed data stream processing systems is using
operator migration to handle highly variable workloads cost-efficiently and adapt to the …

Stream processing pipelines need to handle workload fluctuations (eg, daily patterns,
popularity spikes) by scaling up/down the resources contributed to running jobs. While there …