Sensor Stream Processing Quality Optimization
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Solution Overview
Problem
Data stream processing systems face limitations due to limited memory capacity, data transfer capability, and computational power, leading to data quality deficiencies such as imprecision, completeness issues, and incorrect decisions, exacerbated by sensor failures and inaccuracies.
Innovation Solution
A method for data quality-driven optimization that iteratively tests configurations of the data processing path to identify settings that meet predefined quality requirements, using a secondary processing path to optimize parameters such as sampling, interpolation, and group sizes, and applying Evolution Strategy for multi-objective optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data stream volume is reduced to address system constraints, then computational power and memory capacity requirements are reduced, but data quality and information completeness deteriorate
Solution Approach 1:
The system dynamically adjusts processing parameters such as sampling rates, aggregation windows, and filtering thresholds based on data quality metrics and system resource availability. This allows the system to optimize the balance between computational load and data quality by changing operational parameters rather than simply reducing data volume.
Solution Approach 2:
The patent implements adaptive data processing where the system continuously monitors data quality dimensions and system resource usage, then dynamically adjusts processing intensity, sampling rates, and aggregation levels. This dynamic approach allows the system to maintain optimal data quality when resources are available while automatically reducing processing load when constraints are encountered.
2Productivity
If data processing is optimized for speed and volume reduction, then productivity increases, but measurement precision and data accuracy deteriorate
Solution Approach 1:
The system applies different processing quality levels to different data streams or data segments based on their importance, source reliability, and application requirements. Critical data streams receive higher processing quality with more rigorous validation and lower aggregation levels, while less critical streams can undergo more aggressive optimization, thus maintaining overall productivity while preserving accuracy where needed.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor data quality metrics including accuracy, precision, and completeness. This feedback is used to adjust processing parameters in real-time, ensuring that productivity optimizations do not degrade data quality below acceptable thresholds. The system can automatically reduce processing speed or increase validation when quality degradation is detected.
3Reliability
If complex data processing configurations are implemented to meet quality requirements, then data quality improves, but device complexity increases
Solution Approach 1:
The patent divides the data processing pipeline into modular, independently configurable stages such as filtering, aggregation, enrichment, and validation. Each stage can be selectively enabled or configured based on data quality requirements and system capabilities. This segmentation allows complex quality requirements to be met through composition of simpler modular operations rather than a single complex processing path.
Solution Approach 2:
The system performs preliminary quality assessment and configuration selection before actual data processing begins. By pre-evaluating data sources, determining required quality dimensions, and selecting appropriate processing configurations in advance, the system avoids implementing unnecessarily complex processing paths while ensuring quality requirements are met.
Data Source
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AI summary
A system and method to perform data quality driven optimization of data are described. In one embodiment, a method is presented to iteratively test configurations of a data processing path until a configuration that processes data to predefined quality requirements is identified. In one embodiment, a system is presented. The system includes a data quality initialization module (404), a primary data stream processing module (406) and an optimization module (408) that is incorporated in a memory chip on a computer processor.