Abstract
IoT (Internet of Things) streaming data has increased dramatically over the recent years and continues to grow rapidly due to the exponential growth of connected IoT devices. For many IoT applications, fast stream query processing is crucial for correct operations. To achieve better query performance and quality, researchers and practitioners have developed various types of query execution models—purely cloud‐based, geo‐distributed, edge‐based, and edge‐cloud‐based models. Each execution model presents unique challenges and limitations of query processing optimizations. In this work, we provide a comprehensive review and analysis of query execution models within the context of the query execution latency optimization. We also present a detailed overview of various query execution styles regarding different query execution models and highlight their contributions. Finally, the paper concludes by proposing promising future directions towards advancing the query executions in the edge and cloud environment.
Highlights
Recent advancements in the Internet of Things (IoT) domain have led to the production of a large number of internet-connected devices
Query output transmission may span multiple locations depending on the query scenario
The geodistributed query processing system translates the query to the directed acyclic graph (DAG) of stages comprising graph vertices as query operators and edges as data flows between query operators [10]
Summary
Recent advancements in the Internet of Things (IoT) domain have led to the production of a large number of internet-connected devices These IoT devices emit millions of streaming events [1]. The stream processing applications analyze the streaming events for the extraction of meaningful insights These real-time insights serve as the decisionmaking points for many IoT and E-commerce applications in various domains such as online business, retail, stock markets, manufacturing, and healthcare. Streaming analytics has emerged as a real-time analytics paradigm that goes well with the processing of latency-sensitive IoT applications. These applications span various IoT domains such as smart healthcare, smart traffic management system, smart cities, energy, and retail sectors. It typically applies the time-based windows for the real-time computation of data
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