Multidimensional Combination Data Generation for Configuration Analysis
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Solution Overview
Problem
The rapid increase in the number of multidimensional combinations related to objects, such as server spaces and phone configurations, results in a scarcity of data for many combinations, making it difficult to determine the optimal configuration for deployment, recommendation, or production, as data is often unavailable for untested or unused configurations.
Innovation Solution
A method and system for generating multidimensional combination data by computing all possible combinations of dimension members, normalizing values, and adjusting control components based on identified properties, using a processor and memory, to derive logically reasonable data for missing combinations from available data, while accommodating exclusions and weighting dimensions appropriately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all possible multidimensional combinations are collected and analyzed, then comprehensive data coverage is achieved, but data scarcity for untested configurations persists
Solution Approach 1:
The system performs preliminary actions by collecting actual data for only some multidimensional combinations before analysis, then uses the generated multidimensional combination data to represent untested configurations. This allows comprehensive analysis without requiring physical testing of all possible combinations, resolving the contradiction between data coverage and configuration flexibility.
Solution Approach 2:
The system creates copies of actual configuration data by generating synthetic multidimensional combination data that represents untested configurations. These generated data copies allow analysis of configurations that were never physically implemented, achieving comprehensive data coverage while maintaining the ability to analyze flexible, untested configurations.
2Reliability
If data is collected for all possible multidimensional combinations, then complete analysis is possible, but the quantity of data required becomes unmanageable
Solution Approach 1:
The system performs preliminary data collection for only some multidimensional combinations rather than all possible combinations. The generated multidimensional combination data then represents the untested configurations, enabling complete analysis with a manageable subset of actual data combined with synthesized data.
Solution Approach 2:
The system takes partial action by collecting actual data for only some configurations rather than all possible combinations. The generated data compensates for the untested configurations, providing sufficient information for reliable analysis without requiring excessive data collection.
3Loss of information
If multidimensional combination data is generated for untested configurations, then data availability improves, but data accuracy for generated combinations may decrease
Solution Approach 1:
The system creates data copies by generating multidimensional combination data that mirrors the structure and relationships of actual tested configurations. This copying approach maintains data accuracy by preserving the logical relationships and patterns observed in tested configurations, while extending data availability to untested configurations.
Solution Approach 2:
The system applies parameter changes by generating data for untested configurations based on the patterns and relationships observed in tested configurations. This allows data availability to extend to new parameter combinations while maintaining accuracy through consistent application of observed relationships.
Data Source
AI summary
A set of available values is obtained corresponding to a set of data fields associated with a dimension member in a set of dimension members corresponding to a dimension in a set of dimensions of an object. Each possible combination of dimension members is computed for the object. For a data field of a dimension member of a dimension, a normalized value is computed based on values of the data field for each dimension member in the dimension. A combined data field value is computed for a possible combination of the object as a product of the normalized value of the data field in each dimension member that participates in the possible combination. A set of combined data field values corresponding to all possible combinations is analyzed to identify a possible combination having a property. A control component of a physical environment is adjusted according to the identified possible combination.