Binary Decision Diagrams for Sensor Data Querying
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Solution Overview
Problem
Existing techniques face challenges in efficiently processing and analyzing large volumes of sensor data, particularly in representing and querying sensor data in a manner that allows for effective storage and retrieval of relevant information.
Innovation Solution
The system represents sensor data using characteristic functions stored as binary decision diagrams, allowing for efficient querying and annotation, and transforms Boolean functions into arithmetic functions to determine equivalence through hash code calculation.
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 querying efficiency deteriorates
Solution Approach 1:
The patent transforms sensor data from traditional storage formats into a mathematical function representation (characteristic function). This parameter change converts discrete data points into a continuous mathematical model, enabling efficient querying through function evaluation rather than exhaustive data scanning. The characteristic function f(x) represents the entire dataset, allowing rapid retrieval by evaluating the function at specific query points.
Solution Approach 2:
The patent replaces traditional mechanical data storage and retrieval systems with a mathematical computation system. Instead of physically storing and searching through large volumes of sensor data, the system uses mathematical functions and algorithms to represent and query the data. This substitution enables efficient processing by replacing mechanical data access with computational function evaluation.
2Quantity of substance
If large volumes of sensor data are stored, then data completeness is improved, but data analysis difficulty increases
Solution Approach 1:
The patent extracts the essential characteristics of sensor data and represents them through a characteristic function. Instead of storing and analyzing every individual data point, the system extracts the underlying pattern or function that generates the data. This extraction reduces analysis complexity while preserving the ability to retrieve complete information through function evaluation.
Solution Approach 2:
The characteristic function serves multiple purposes simultaneously: it represents the entire dataset, enables querying for specific values, supports data analysis, and allows for data reconstruction. This universal representation eliminates the need for separate storage and analysis mechanisms, reducing overall system complexity while handling large data volumes.
3Measurement precision
If sensor data is represented by detailed samples, then measurement precision is maintained, but storage and retrieval efficiency deteriorates
Solution Approach 1:
The patent performs preliminary action by transforming sensor data into a characteristic function representation during data collection. This pre-processing step converts raw samples into a mathematical model that preserves measurement precision while enabling rapid retrieval. The characteristic function is prepared in advance, so subsequent queries can be answered immediately through function evaluation without reprocessing the original data.
Data Source
AI summary
According to certain embodiments, a search query for a search of samples of sensor data is received. The search query indicates one or more requested values of one or more data parameters. The samples are represented by a characteristic function indicating whether a given binary representation represents a sample. A query function representing the one or more requested values is formulated. The query function and the characteristic function are used to identify one or more samples that have the one or more requested values.