Sensor Data Binary Decision Diagram Representation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques face challenges in efficiently processing and analyzing large volumes of sensor data, particularly in representing and querying sensor measurements effectively.
Innovation Solution
The method involves representing sensor data as a set of minterms, generating a characteristic function to determine membership in a set of minterms, and using binary decision diagrams to store and query this data, allowing for efficient storage and annotation of sensor data and transformation of Boolean functions into arithmetic functions for equivalence determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data is stored in traditional formats, then data volume capacity is maintained, but data processing and analysis efficiency deteriorates
Solution Approach 1:
The patent transforms sensor data from traditional continuous formats into discrete Boolean functions by changing the parameter representation. Each sensor reading is converted into a Boolean variable, and combinations of readings form Boolean expressions that can be efficiently processed using binary decision diagrams, thereby improving processing efficiency while managing complexity through mathematical transformation
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with mathematical Boolean function manipulation. Instead of processing raw sensor data through complex algorithms, the system uses Boolean algebra and binary decision diagrams to represent and query sensor data, substituting computational mechanics with mathematical structures that enable more efficient analysis
2Measurement precision
If detailed sensor measurements are retained, then measurement precision is improved, but data volume increases making analysis difficult
Solution Approach 1:
The patent extracts the essential logical structure from detailed sensor measurements by converting continuous data into discrete Boolean states. Instead of storing and processing all raw measurement values, the system extracts the critical decision-making information into Boolean functions, retaining measurement precision where needed while reducing overall data volume for analysis
Solution Approach 2:
The patent segments sensor data into discrete Boolean variables representing specific measurement conditions. By dividing continuous sensor readings into discrete logical states (true/false, 0/1), the system maintains the precision needed for accurate measurement representation while organizing data into manageable segments that can be efficiently processed using binary decision diagrams
Data Source
AI summary
According to certain embodiments, a set of samples of sensor data is accessed. The set of samples records measurements taken by one or more sensors. Each sample is represented as a minterm to yield a set of minterms. A characteristic function is generated from the set of minterms. The characteristic function indicates whether a given minterm is a member of the set of minterms.