Partitioning Medical Binary Decision Diagrams for Sensor Data Analysis

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Solution Overview

Problem

Current sensor networks and binary decision diagrams face challenges in efficiently analyzing and optimizing large datasets from diverse medical sensors, particularly in determining correlations and optimizing size and compression rates, which affects data storage and processing efficiency.

Innovation Solution

The integration of reduced ordered binary decision diagrams (ROBDDs) and advanced data processing methods within sensor networks allows for efficient data analysis, correlation determination, and size optimization by partitioning and combining BDDs, enhancing data storage and processing capabilities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If large datasets from diverse medical sensors are analyzed using traditional methods, then comprehensive data analysis is achieved, but processing efficiency and storage capacity deteriorate

Engineering Contradiction:

Improveprocessing efficiencyVSAvoiddata storage capacity

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The patent partitions the large medical sensor dataset into multiple smaller partitions, which are then represented as separate Binary Decision Diagrams (BDDs). This segmentation allows the system to process and store data more efficiently by working with manageable units rather than a monolithic dataset, directly resolving the contradiction between comprehensive analysis and processing efficiency.

Inventive Principle:

Principle #1Segmentation

2Productivity

If the size of Binary Decision Diagrams is reduced through partitioning, then processing efficiency improves, but analysis accuracy may deteriorate

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidanalysis accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent combines multiple partitioned BDDs back together to form a comprehensive representation of the entire dataset. This merging process ensures that while individual partitions are processed efficiently, the collective analysis maintains the accuracy and completeness required for reliable medical data interpretation, thus resolving the contradiction between processing efficiency and analysis accuracy.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS9177247B2Partitioning medical binary decision diagrams for analysis optimization

Publication Date: 2015.11.03 FUJITSU LTD

AI summary

In particular embodiments, a method includes accessing a first binary decision diagram (BDD) representing data streams from sensors, selecting portions from the first BDD based on ease-of-analysis, and constructing a plurality of sub-BDDs by partitioning the first BDD, wherein the sub-BDDs comprises a first sub-BDD representing the selected portions, and second sub-BDDs representing the non-selected portions.