Complex Event Processing Pattern Detection via Relational Join Query Optimization
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Solution Overview
Problem
Complex event processing systems face challenges in efficiently detecting patterns in massive data streams due to high computational costs and latency, particularly when dealing with complex patterns and large volumes of events from multiple sources.
Innovation Solution
A method and system that convert a template defining the pattern of events into a join query for a relational database, selecting a cost function to optimize the computation cost, and identifying a preferred execution plan to reduce detection latency and increase throughput by converting database operations into an ordered set of patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex event processing systems process massive data streams to detect patterns, then detection accuracy is improved, but computational cost increases
Solution Approach 1:
The patent segments the pattern detection process into multiple stages: event generation from data streams, pattern matching against templates, and result aggregation. This segmentation allows the system to process events in manageable units and apply optimizations at each stage, reducing overall computational cost while maintaining detection accuracy across massive data streams
Solution Approach 2:
The patent performs preliminary actions by pre-compiling pattern templates and pre-processing data stream events into standardized formats before pattern matching. This preliminary preparation reduces the computational complexity during actual pattern detection, enabling accurate pattern recognition in massive data streams with lower real-time processing costs
2Measurement precision
If complex event processing systems process massive data streams to detect patterns, then detection accuracy is improved, but detection latency increases
Solution Approach 1:
The patent implements continuous pattern matching operations that process data streams in real-time without interruption. The system maintains continuous event generation and pattern matching operations, ensuring that detection accuracy is preserved across the entire data stream while minimizing detection latency through uninterrupted processing
Solution Approach 2:
The patent segments the data stream processing into continuous batches or windows, allowing parallel processing of different segments simultaneously. This segmentation enables the system to maintain continuous detection operations with reduced latency, as multiple segments can be processed in parallel while preserving overall detection accuracy
3Reliability
If complex event processing systems handle large volumes of events from multiple sources, then detection completeness is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal event processing framework that handles events from multiple diverse sources through a common architecture. The system uses standardized event templates and unified pattern matching mechanisms that can process various event types uniformly, improving detection completeness across multiple sources while reducing system complexity through consolidation rather than separate processing paths for each source
4Productivity
If complex event processing systems optimize computation cost, then operational efficiency is improved, but detection latency may increase
Solution Approach 1:
The patent applies partial action by implementing incremental pattern matching that processes events in stages rather than requiring complete pattern verification before reporting. This allows the system to achieve operational efficiency through selective processing while maintaining acceptable detection latency by reporting partial matches that can be refined later
Data Source
AI summary
A method for detecting a pattern of events, comprising: receiving a template defining the pattern of events to detect; producing a join query for a relational database from the template; selecting a cost function for the join query, wherein a computation cost of the join query is equivalent to a computation cost of matching between a plurality of events from a plurality of streams of events and the template; identifying a preferred plan for executing the join query according to a minimal value of the cost function; producing an ordered set of a plurality of patterns, according to the preferred plan, by converting a plurality of database operations of the preferred plan into the ordered set of the plurality of patterns; and using the ordered set as an input for detecting a plurality of matches between the plurality of patterns and the plurality of events.