Histogram-Preserving Digests for Scalable Manufacturing Data Analysis
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Solution Overview
Problem
Current manufacturing system data analysis technologies face challenges in efficiently processing high-volume data while minimizing storage and response time, often requiring extensive data storage and computational resources, which limits flexibility and scalability for post-hoc analyses.
Innovation Solution
The method employs histogram-preserving digest compression to generate and store digests, allowing for rapid analysis of process parameter data by selecting and merging digests, which reduces data storage needs and computational complexity, enabling efficient computation of descriptive statistics and flexible post-hoc analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manufacturing system data analysis methods are used, then comprehensive data processing is achieved, but storage requirements and computational resources increase significantly
Solution Approach 1:
The patent extracts only the essential statistical characteristics from raw manufacturing data by computing digests (summary statistics) that capture the core information needed for analysis. This extraction approach maintains analysis accuracy while dramatically reducing the volume of data that needs to be stored and processed, as only the computed digests are retained rather than the complete raw datasets.
Solution Approach 2:
Instead of storing all raw data and computing statistics when needed, the patent inverts the approach by pre-computing and storing only the essential statistical digests. This inversion allows the system to answer analytical queries using the pre-computed summaries rather than reprocessing vast amounts of raw data, thereby reducing storage requirements while maintaining analytical capability.
2Adaptability or versatility
If extensive raw data is stored for post-hoc analysis, then analysis flexibility is improved, but response time and computational complexity increase
Solution Approach 1:
The patent performs preliminary computation of statistical digests from raw manufacturing data before any analytical queries are made. By pre-computing these summary statistics and storing them in a structured format, the system prepares the data in advance for various types of post-hoc analysis, enabling rapid response to analytical queries without needing to reprocess raw data each time.
Solution Approach 2:
The patent segments the manufacturing data into hierarchical groups based on dimensional relationships (e.g., by machine, by time period, by product type). This segmentation organizes the pre-computed digests in a structured manner that allows flexible querying at different levels of aggregation, maintaining adaptability for various analysis scenarios while keeping response times low through efficient data organization.
3Reliability
If high-volume manufacturing data is processed in real-time, then data completeness is improved, but computational resources and system complexity increase
Solution Approach 1:
The patent extracts essential statistical features from high-volume manufacturing data streams and stores only these extracted digests rather than the complete raw data. This extraction maintains the reliability needed for comprehensive analysis by preserving key statistical properties while dramatically reducing the computational burden of storing and processing the full dataset.
Solution Approach 2:
The patent transforms raw manufacturing data into a different parameter representation by computing statistical digests (summary statistics). This parameter change converts voluminous raw measurements into compact statistical descriptors that are easier to store, process, and query, thereby reducing system complexity while maintaining data completeness for analytical purposes.
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.