Manufacturing Data Digests for Fast Post-Hoc Query Analysis
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Solution Overview
Problem
Conventional manufacturing system data analysis methods struggle with high computational complexity and storage demands due to the need to store and process large volumes of raw data, limiting the ability to conduct arbitrary post-hoc analyses and increasing response times.
Innovation Solution
The use of histogram-preserving digest compression to generate and store digests of manufacturing system data, allowing for rapid analysis by selecting and merging low-resolution digests to answer queries, while minimizing storage and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If raw manufacturing system data is stored and processed using conventional methods, then complete data is available for analysis, but storage demands and computational complexity increase significantly
Solution Approach 1:
The patent extracts only the essential statistical characteristics (histogram data) from the raw manufacturing data, storing compressed representations rather than complete raw data. This extraction approach maintains the information needed for analysis while removing redundant data, thereby reducing storage demands and computational complexity.
Solution Approach 2:
The patent transforms raw manufacturing data into histogram-based statistical parameters, changing the data representation from individual data points to aggregated distribution characteristics. This parameter transformation enables efficient storage and faster processing while preserving the essential information needed for post-hoc analysis.
2Loss of information
If large volumes of raw manufacturing data are stored, then comprehensive analysis is possible, but storage demands increase
Solution Approach 1:
The patent extracts only the essential statistical characteristics (histogram data) from the raw manufacturing data, storing compressed representations rather than complete raw data. This extraction approach maintains the information needed for analysis while removing redundant data, thereby reducing storage demands and computational complexity.
Solution Approach 2:
The patent transforms raw manufacturing data into histogram-based statistical parameters, changing the data representation from individual data points to aggregated distribution characteristics. This parameter transformation enables efficient storage and faster processing while preserving the essential information needed for post-hoc analysis.
3Loss of information
If conventional data processing methods are used, then all data can be analyzed, but response times increase
Solution Approach 1:
The patent performs preliminary aggregation of manufacturing data into histogram digests during data collection, pre-computing statistical characteristics before queries are made. This preliminary action enables rapid response to analysis requests without requiring processing of raw data at query time, significantly reducing response times.
Solution Approach 2:
The patent transforms raw manufacturing data into histogram-based statistical parameters, changing the data representation from individual data points to aggregated distribution characteristics. This parameter transformation enables efficient storage and faster processing while preserving the essential information needed for post-hoc analysis.
Data Source
AI summary
A method for manufacturing system data analysis, preferably including receiving a query, determining a set of digests, and/or determining a query result. The method can optionally include merging the set of digests and/or providing the query result. A system for manufacturing system data analysis, preferably including a set of machines, a plurality of digests, and/or a set of computing systems.