Stream Processing for Real-Time Incident Identification
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Solution Overview
Problem
Existing systems face challenges in identifying incidents in real-time data streams from geographically distributed sensors, particularly in distinguishing between normal and abnormal events, which can lead to delayed response or incorrect intervention in critical situations.
Innovation Solution
A stream processing system that receives event block objects with measurement data and time values, computes similarity measures with reference incident signatures, and identifies incidents by selecting the best matching signature based on a threshold comparison, allowing for timely and accurate incident classification and notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time incident identification is implemented using similarity comparison with reference signatures, then incident detection accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The system pre-computes and stores reference incident signatures in a database before actual incident detection is needed. When an incident occurs, the system only needs to compare current event data against these pre-stored references rather than analyzing all historical data in real-time, significantly reducing processing time while maintaining high detection accuracy.
Solution Approach 2:
The system extracts only the critical features and time-windowed data points from the continuous event stream that are relevant for incident identification. By filtering and selecting only the necessary measurement data values within specific time windows, the system reduces the amount of data that needs to be processed and compared, thereby decreasing computational complexity and processing time.
2Reliability
If continuous monitoring of event streams from geographically distributed sensors is implemented, then system reliability is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system divides the continuous event stream into discrete time windows and segments the data processing into modular components: data collection from distributed sensors, time-windowed extraction, similarity comparison against reference signatures, and incident notification. This segmentation allows each component to be optimized independently and simplifies the overall processing architecture despite the distributed nature of the sensors.
Solution Approach 2:
The system introduces an intermediary processing layer that sits between the distributed sensors and the incident response system. This intermediary layer aggregates data from multiple geographically distributed sensors, performs the similarity comparison calculations, and filters out false positives before triggering responses, thereby reducing the complexity at the sensor level and the response system level.
3Productivity
If threshold-based incident classification is used to distinguish consequential from non-consequential events, then response efficiency is improved, but false positive rates may increase
Solution Approach 1:
The system continuously refines the reference incident signatures and threshold values based on feedback from actual incident responses and false positive analyses. By learning from past performance and adjusting the similarity thresholds and reference data, the system maintains high response efficiency while reducing false positive rates over time.
Data Source
AI summary
A computing device to perform incident identification on streamed event data is provided. An event block object is received. The event block object includes a measurement data value generated by a sensor. A plurality of measurement data values and associated time values are extracted for the plurality of event block objects that occur during a time window. A similarity measure is computed between the measurement data values and each of a plurality of reference incident signatures. A best matching reference incident signature is selected from the similarity measures. The similarity measure for the selected best matching reference incident signature is compared to a threshold value. When the selected similarity measure satisfies the threshold value, a type of incident for the extracted measurement data values is identified as a type of incident defined for the selected best matching reference incident signature and is output to an incident information subscribing device.