Complex Event Processing for Sensor Stream Data.

Area of Science:

• Computer Science
• Data Science
• Internet of Things

Background:

• Stream data from Internet of Things (IoT) sensors necessitates real-time information detection via complex event processing (CEP).
• Existing CEP methods are time-consuming due to a lack of consideration for operator similarity and redundancy.
• Complex events are formed by combining primitive events using various operators.

Purpose of the Study:

• To propose a novel CEP method that addresses the inefficiency of existing approaches by handling similar and redundant operations.
• To reduce the computational cost associated with processing large volumes of real-time sensor data.

Main Methods:

• A new CEP method is proposed that identifies and consolidates similar and redundant operations within event streams.
• Similar operations are converted into a single virtual operator.
• Redundant operations on identical events are merged into one operator.
• The event query tree is reconstructed with these optimized operators for efficient complex event detection.

Main Results:

• The proposed method significantly reduces the cost of comparison and inspection for similar and redundant operations.
• Overall processing cost for real-time stream data is decreased.
• Experimental evaluation demonstrates superior performance compared to existing CEP methods.

Conclusions:

• The novel CEP method effectively optimizes operations, leading to reduced processing costs for real-time sensor data.
• This approach enhances the efficiency of information detection in IoT environments.
• The findings suggest a more scalable and performant solution for real-time data analysis.