Data Model Indexing for Query Efficiency
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Solution Overview
Problem
Current database systems face inefficiencies in data retrieval due to the need to recode or recompile software when database architecture changes, and they often require direct reference to the underlying data source schema for querying, which can be cumbersome and inefficient.
Innovation Solution
Implementing a data model index that correlates previous model queries and their responses, allowing for efficient retrieval of similar queries by referencing the data model schema rather than the underlying data source schema, and utilizing a cache for prior query results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system references the underlying data source schema for querying, then the query can access the actual data structure, but the system becomes cumbersome and inefficient when database architecture changes require recoding or recompilation
Solution Approach 1:
The patent introduces a data model index as an intermediary layer between the query system and the underlying data source schema. This index correlates previous model queries and their responses, allowing the system to reference the data model schema rather than directly accessing the underlying data source schema. When database architecture changes occur, the index can be updated without requiring recoding or recompilation of the entire system, thus resolving the contradiction between query accuracy and adaptability
Solution Approach 2:
The system pre-computes and stores query results in the data model index before they are needed. By correlating previous model queries and their responses in advance, the system can quickly retrieve cached results for similar queries without重新 accessing the underlying data source, improving efficiency and reducing the impact of architecture changes
2Reliability
If the system performs full data model queries to ensure accurate results, then query accuracy is maintained, but retrieval efficiency decreases due to repeated processing
Solution Approach 1:
The system pre-computes query results and stores them in the data model index with correlation information. When a similar query is encountered, the system can retrieve the pre-computed results from the index rather than performing a full data model query again, maintaining accuracy while significantly improving retrieval efficiency
Solution Approach 2:
The system uses the correlated query responses in the index as feedback to determine whether a new query can be satisfied by existing results. By comparing the current query against previous queries and their cached results, the system can avoid redundant processing while ensuring accurate results are returned
Data Source
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AI summary
The use of a data model index for assisting in efficient response to a data model query. The data model query identifies the data model itself and references the schema of the data model, rather than referencing the schema of the underlying data source. The index assists by correlating previous model queries on the data model and associated response to those previous model queries. Accordingly,if a similar model query is encountered, the response from the previous model query may still be considered fresh, the index may be used to more efficiently retrieve the results for the similar model query.A data management system may include multiple data models, each having a corresponding data model index.