Software-Embedded Tuple Dynamic Grouping for Real-Time Stream Processing
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Solution Overview
Problem
Existing computing systems are inadequate for real-time analysis of large volumes of data from diverse, unstructured sources, leading to inaccurate decisions due to reliance on outdated structural databases and inability to handle continuous streams of information effectively.
Innovation Solution
A smart stream computing environment that embeds software code within tuples, allowing for dynamic processing and operation based on embedded segments, enabling real-time analysis and flexible tuple processing power adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional structural databases are used for data analysis, then system simplicity is maintained, but real-time processing capability and decision accuracy deteriorate due to reliance on outdated information
Solution Approach 1:
The patent applies dynamics by transforming static database structures into dynamic stream processing capabilities. The system continuously processes data streams in real-time rather than relying on fixed, periodic database updates. This allows the system to adapt to changing data conditions dynamically, improving real-time processing capability while managing complexity through structured stream processing frameworks.
Solution Approach 2:
The patent changes the fundamental parameter of data accessibility from periodic (database-driven) to continuous (stream-driven). By implementing stream computing with real-time data processing, the system transforms how data is accessed and analyzed, enabling decisions based on current information rather than outdated snapshots, thus improving productivity without excessive complexity increase.
2Adaptability or versatility
If software code is embedded within tuples for dynamic processing, then processing flexibility and adaptability improve, but tuple structure complexity and processing overhead increase
Solution Approach 1:
The patent applies nesting by embedding software code segments directly within tuple structures. This allows the code to be contained within the data container itself, enabling dynamic processing logic to travel with the data through the stream processing pipeline. The nested structure provides flexibility while maintaining a manageable complexity through hierarchical organization of code and data.
Solution Approach 2:
The patent implements self-service by enabling tuples to carry their own processing instructions through embedded code segments. Each tuple can autonomously execute its associated code at appropriate processing points in the stream, reducing the need for external control logic and simplifying the overall system architecture despite the increased tuple structure complexity.
3Measurement precision
If large volumes of data from diverse sources are processed in real-time, then decision accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-compiling and optimizing stream processing logic before data arrives. The system prepares processing pipelines, filters, and aggregation operations in advance, so that when data streams arrive, they can be processed efficiently without ad-hoc computation overhead. This reduces processing time while maintaining the ability to handle large volumes of diverse data for accurate real-time decisions.
Data Source
AI summary
A stream application receives a stream of tuples to be processed by a plurality of processing elements. The plurality of processing elements operating on one or more compute nodes. Each processing element has one or more stream operators. The stream application assigns one or more processing cycles to one or more segments of software code. The segments of software code are embedded in a tuple of the stream of tuples. The software-embedded tuple identifies a set of target tuples based upon operation criteria. The set of target tuples are a part of the stream of tuples. The software-embedded tuple performs an operation based on the set of identified target tuples.