Measurement Data Grouping for Faster Database Analysis
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Solution Overview
Problem
The management of large volumes of measurement data in relational databases is resource-intensive due to the need for frequent sampling and storage of each data point, leading to slow database performance and increased resource expenditure.
Innovation Solution
The method involves grouping measurement data into data units, where each unit includes an interpolation point measurement datum and multiple adjacent data points, reducing the number of data records required in the database by a factor corresponding to the number of data points within the unit, and storing measurement and time values separately in tuples for efficient retrieval and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If measurement data are stored in a relational database with one data record per measurement datum, then the data can be stored systematically, but the database takes up high data volume and reacts slowly due to the large number of data records
Solution Approach 1:
Multiple measurement data records are merged into a single data unit that contains an array of measurement data. Instead of storing each measurement datum as a separate row in the database, the patent combines multiple consecutive measurement data points into one consolidated record, thereby reducing the total number of database operations and improving performance.
Solution Approach 2:
The measurement data are segmented into data units with a specific structure containing an array of measurement data. Each data unit represents a segment of the continuous measurement stream, organized in a way that optimizes database storage and retrieval operations while maintaining data integrity.
2Measurement precision
If measurement data are acquired with high sampling rate to analyze the measurement data, then the analysis precision is improved, but the data volume increases and requires correspondingly high memory capacity
Solution Approach 1:
Multiple measurement data points acquired at high sampling rates are merged into consolidated data units. This reduces the overall data volume stored in the database while preserving the high-resolution measurement data needed for precise analysis, as the data are combined rather than discarded.
3Productivity
If the number of data records is reduced by grouping measurement data into data units, then the database management overhead is reduced, but the data structure becomes more complex
Solution Approach 1:
The data are segmented into standardized data units with a defined structure containing an array of measurement data. This segmentation approach, while introducing some structural complexity, enables more efficient database operations by reducing the number of individual records that need to be managed.
Solution Approach 2:
The data structure parameters are changed from individual measurement records to consolidated data units with arrays. This parameter transformation optimizes the balance between database management efficiency and data accessibility, allowing for reduced overhead while maintaining analytical capabilities.
Data Source
AI summary
The invention relates to a processing, i.e. storing and analyzing, of measurement data. In order to store the measurement data, respective data units or data sets are created for sampling points, a plurality of measurement data temporally adjacent to the sampling-point measurement datum being stored within the data unit. A time interval between time values of the sampling-point measurement data of two consecutive data units at least is set approximately to a multiple of a sampling time interval, i.e. of a reciprocal of the measurement-value recording rate or sampling rate. The method according to the invention reduces the provision of data units because an individual data unit contains, in addition to a sampling-point measurement date, further measurement data temporally adjacent to the sampling-point measurement date. This measure reduces the management of data sets that is required in a database approximately by a factor which corresponds to the number of measurement data within the data unit designed according to the invention.