Binary Decision Diagrams for Health Data Compression
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Solution Overview
Problem
Current sensor networks and binary decision diagrams face challenges in efficiently processing and analyzing large volumes of health-related data from diverse sources, including redundant data and data corruption, which hinders accurate health monitoring and analysis.
Innovation Solution
The integration of sensor networks with reduced ordered binary decision diagrams (ROBDDs) for data compression and analysis optimization, allowing for efficient data processing, detection of malfunctions, and correlation of health data streams, while utilizing hashing techniques for data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor networks collect and store large volumes of health data from diverse sources, then data completeness and monitoring accuracy are improved, but data processing time and system complexity increase
Solution Approach 1:
The patent segments health data into multiple dimensions (temporal, spatial, categorical) and processes each segment separately using specialized data structures. This allows the system to handle large volumes of diverse health data without overwhelming processing complexity, as each segmented portion can be managed independently with optimized algorithms.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers that mediate between raw sensor data and final analysis results. These intermediaries organize and pre-process data before it reaches the main analysis system, reducing the complexity burden on the core processing architecture while maintaining data completeness.
2Speed
If sensor networks process and analyze data in real-time, then health monitoring speed is improved, but energy consumption and computational load increase
Solution Approach 1:
The patent implements periodic processing intervals where data is analyzed at optimized frequencies based on health parameter importance and change rates. Critical parameters are processed more frequently while stable parameters use lower update rates, maintaining real-time monitoring capability while reducing overall computational load and energy consumption across the sensor network.
Solution Approach 2:
The patent applies partial processing by focusing computational resources on the most critical health parameters and anomaly detection rather than uniformly processing all data at maximum speed. This selective approach maintains adequate monitoring speed for health-critical functions while reducing unnecessary computational energy consumption on less urgent data processing tasks.
3Productivity
If binary decision diagrams are used to represent health data states, then data analysis efficiency is improved, but memory requirements and diagram complexity increase
Solution Approach 1:
The patent merges multiple binary decision diagrams that represent different health parameters and time periods into a unified, integrated BDD structure. This consolidation eliminates redundant nodes and memory allocations across separate diagrams, reducing overall memory requirements while maintaining comprehensive health data analysis capability through the combined structure.
Solution Approach 2:
The patent applies local quality optimization by creating specialized BDD representations for different regions of the health data space based on their specific characteristics. Critical health states use more detailed BDD structures for precise analysis, while less critical regions use simplified representations, optimizing the balance between analysis efficiency and memory consumption in different local areas of the data space.
4Quantity of substance
If data compression techniques are applied to health data, then storage efficiency is improved, but data integrity and detection accuracy may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where compressed health data is continuously validated against expected physiological ranges and patterns. When compression artifacts or anomalies are detected, the system adjusts compression parameters or triggers additional verification, ensuring data integrity is maintained while achieving efficient storage through adaptive compression that preserves critical health information.
Data Source
AI summary
In particular embodiments, a method includes receiving a query for data in data sets that are within a specified range, constructing a first binary decision diagram (BDD) representing the specified range, and constructing a third BDD representing the data in the specified range by performing an AND operation between the first BDD and a second BDD representing the data sets.