Optimizer for Associative Memory Query Criteria
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Solution Overview
Problem
Users face difficulties in formulating optimal queries for associative memories, especially when they are not subject matter experts, leading to sub-optimal comparisons and inefficiencies in data searches.
Innovation Solution
A system and method that utilize an optimizer to generate a multi-dimensional criteria file from input criteria, converting them into numerical representations associated with expert weights, allowing for optimized attribute selection and comparison within associative memories, enabling non-experts to leverage expert knowledge for more relevant and accurate entity comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users formulate queries without subject matter expertise, then ease of operation is improved, but measurement precision of comparison criteria deteriorates
Solution Approach 1:
The patent introduces an optimizer as an intermediary component that automatically generates optimal comparison criteria from user input. The optimizer acts as a mediator between the user's simple query input and the complex associative memory search requirements, translating user intent into precise multi-dimensional criteria without requiring users to have subject matter expertise.
Solution Approach 2:
The system enables self-service by allowing the optimizer to automatically generate optimized comparison criteria based on the input criteria and existing data in the associative memory. The system serves itself by learning from the data structures and relationships already present in the memory, eliminating the need for manual expert intervention in each query formulation.
2Measurement precision
If precise criteria are used for entity comparison, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The optimizer automatically generates precise comparison criteria by analyzing the input criteria and the data structures within the associative memory. The system self-services by deriving the necessary multi-dimensional criteria from the existing data relationships, eliminating the need for manual configuration of complex search parameters and reducing the perceived complexity for users.
Solution Approach 2:
The system performs preliminary analysis of the input criteria and data relationships before executing the search. The optimizer pre-processes the query requirements and generates the optimal comparison criteria in advance, so that when the search is executed, the complex precision requirements are already resolved and ready for efficient processing.
3Productivity
If multi-dimensional criteria are generated automatically, then productivity is improved, but use of energy increases
Solution Approach 1:
The optimizer dynamically adjusts the parameters of the comparison criteria based on the input and the data in the associative memory. By changing the dimensional parameters and weights of the criteria, the system can optimize search efficiency for different query types without requiring a complete reconfiguration, thereby improving productivity while managing computational energy consumption through adaptive parameter adjustment.
Data Source
AI summary
A system including an associative memory including a plurality of data and a plurality of associations among the plurality of data. The plurality of data is collected into associated groups. The associative memory is configured to be queried based on at least indirect relationships among the plurality of data. The system also includes an input device in communication with the associative memory, the input device configured to receive an input criteria. The system also includes an optimizer in communication with the input device and the associative memory. The optimizer is configured to generate, using the associative memory, a multi-dimensional criteria file from the input criteria. The optimizer converts the input criteria to numerical representations associated with expert weights and generates the multi-dimensional criteria file to include an optimized plurality of criteria relevant to the input criteria.