Stream Processing System for Real-Time Event Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in quickly detecting higher-level events from large volumes of data received from multiple sensors, requiring extensive data correlation and processing resources.
Innovation Solution
A stream processing system that receives a stream model, stream mapping, and user-specified stream matching patterns to identify co-occurring events within a time window, generating output data elements that specify high-level events and their probabilities, thereby reducing the need for extensive data evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive data correlation and processing is performed to detect higher-level events from multiple sensors, then measurement precision and reliability improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining event patterns, co-occurrence criteria, and time windows before data arrives. The stream processing system is configured with predetermined rules that automatically match incoming data streams against these pre-specified patterns, eliminating the need for extensive post-hoc data correlation and significantly reducing processing time while maintaining detection accuracy
2Reliability
If extensive data correlation is performed across multiple sensor streams, then event detection reliability improves, but computational resources and device complexity increase
Solution Approach 1:
The system segments the complex task of higher-level event detection into distinct, manageable components: individual data streams from multiple sensors are processed separately, each stream is evaluated against specific co-occurrence criteria independently, and results are combined based on pre-defined logical rules. This segmentation reduces device complexity by avoiding the need for a single complex correlation engine while maintaining reliable detection through systematic component integration
Solution Approach 2:
The patent introduces an intermediary layer consisting of pre-defined event patterns and co-occurrence criteria that mediate between raw sensor data and higher-level event detection. This intermediary framework simplifies the processing system by providing a standardized matching mechanism that automatically correlates events across multiple streams without requiring complex real-time computational resources
3Productivity
If real-time event detection is implemented with multiple sensor correlations, then productivity improves, but use of energy and computational resources increase
Solution Approach 1:
The system implements periodic action by evaluating data streams against pre-defined patterns at regular intervals defined by time windows, rather than continuously processing all possible event combinations. The stream processing system checks for co-occurring events within specified temporal boundaries, enabling real-time detection with reduced computational energy consumption by avoiding exhaustive continuous analysis
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting higher level events based on lower level events. In one aspect, a method includes receiving a stream model that defines elements of a data domain. A stream mapping that defines sensor identifiers for real-world sensors and associates each sensor identifier with a respective element of the data domain is received. A user-specified stream matching pattern is received. The stream matching pattern specifies respective sensor identifiers of sensor identifiers of the real-world sensors, and for each sensor identifier, a tuple of data elements of the stream model and, for each tuple of data elements, co-occurrence criteria including at least one time window. A stream of events is obtained from the real-world sensors. A determination is made that two or more events co-occur within the time window and whether the one or more co-occurrence criteria are satisfied.