Data Processing System Using Variable Type Segmentation
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Solution Overview
Problem
Users face difficulties in analyzing and processing large datasets from multiple sources due to the complexity of identifying relevant data and the need for extensive user input, especially as the amount of raw and analyzed data grows over time.
Innovation Solution
A system and method that allow users to easily retrieve, process, and analyze large datasets by associating data sets with variable types and operation definitions, enabling the use of the same operation definitions across different data sets of the same variable type, and allowing selection of related data sets through a related data set designation, simplifying data processing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple sensors and sources is stored and accumulated over time, then the quantity and completeness of available data increases, but the difficulty of identifying and analyzing relevant data increases significantly
Solution Approach 1:
The patent segments the large volume of data into distinct data sets, each associated with specific variable types (e.g., temperature, pressure, flow rate). This segmentation allows users to navigate and analyze data by variable type rather than searching through all accumulated data, directly reducing the difficulty of identifying relevant data while preserving the complete data quantity.
Solution Approach 2:
The patent introduces variable types as an intermediary layer between the raw data and the user. Instead of users directly interacting with individual data points from multiple sensors, the variable type acts as a mediator that groups and organizes data, making it easier to locate and analyze relevant information without losing access to the complete data set.
2Manufacturing precision
If data processing operations are customized for each specific data set, then the precision and relevance of analysis improves, but the user input and complexity of operations increases
Solution Approach 1:
The patent creates variable type definitions that can be universally applied to multiple data sets sharing the same variable characteristics. Once a variable type is defined with its processing operations, this definition can be reused across numerous data sets, maintaining precise and relevant analysis while dramatically reducing user input requirements through this multi-functional approach.
Solution Approach 2:
The patent performs preliminary action by pre-defining variable types and their associated processing operations before actual data analysis occurs. This upfront configuration of variable types with their specific processing requirements eliminates the need for users to repeatedly configure operations for each data set, reducing user input while preserving analysis precision through pre-configured, tailored processing rules.
3Ease of operation
If the same operation definitions are applied across different data sets of the same variable type, then the simplicity and repeatability of operations improves, but the adaptability to handle variable-specific nuances may be reduced
Solution Approach 1:
The patent applies local quality by allowing each variable type to have its own specific processing operations and parameters tailored to its characteristics. While the same variable type (e.g., temperature) can be processed using the same operation definition across different data sets, the operation definition itself contains variable-specific parameters and processing rules that adapt to the local characteristics of each variable type, maintaining both simplicity through reusability and adaptability through customization.
Data Source
AI summary
A system and method for processing data by accessing data sets for a plurality of variables in at least one data store; associating a plurality of the data sets as at least one variable type; storing in a data store a plurality of operation definitions defining a plurality of operations on at least one of said at least one variable type; receiving from a user interface a selection of at least one operation definition and at least one data set of said at least one variable type operated on by the selected at least one operation definition; and processing the at least one data set in response to the selection according to the at least one operation definition to generate a derived data set.