Phrase Indexing for Intent Resolution in Database Analytics
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Solution Overview
Problem
Existing database analytic tools are inefficient, costly, and require substantial configuration and training, making it difficult for businesses to access and analyze large volumes of data effectively.
Innovation Solution
A low-latency database analysis system utilizing a phrase index that resolves user intent by identifying and outputting candidate phrases, reducing resource utilization and cognitive load through intent-resolution using a phrase index, a token index, and a resolved-request index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database analytic tools are used, then data analysis capability is provided, but the tools are inefficient, costly, and require substantial configuration and training
Solution Approach 1:
The system enables users to perform database analysis without requiring external expert assistance or extensive training. The natural language processing and automated intent resolution allow users to independently query and analyze data using everyday language, eliminating the need for specialized knowledge of database schemas or complex query languages.
Solution Approach 2:
The patent introduces an intermediary layer consisting of natural language processing modules, intent resolution systems, and automated query generation components. This intermediary translates user-friendly natural language inputs into optimized database queries, shielding users from the complexity of underlying database structures while maintaining analysis capability.
2Measurement precision
If comprehensive database analysis is performed on large volumes of data, then analytical depth is improved, but resource utilization increases and latency increases
Solution Approach 1:
The system performs preliminary actions by pre-compiling phrase indexes from database schemas and metadata before actual analysis queries are executed. This pre-processing organizes potential query patterns and data relationships in advance, enabling rapid matching and resolution during actual analysis without requiring intensive real-time computation resources.
Solution Approach 2:
The patent segments the database analysis process into distinct modular components: natural language parsing, intent resolution, query generation, and result delivery. Each component handles specific tasks independently, allowing for optimized resource allocation and parallel processing where applicable, thereby reducing overall resource utilization while maintaining analysis accuracy.
3Ease of operation
If natural language processing is implemented for intent resolution, then user accessibility is improved, but system complexity increases
Solution Approach 1:
The natural language processing system is designed with universal functionality to handle multiple types of database queries and analysis tasks through a single unified interface. The intent resolution mechanism recognizes various user intentions (filtering, aggregation, comparison, trend analysis) and routes them through appropriate processing paths, providing diverse capabilities through a common architecture that manages complexity internally while presenting simplicity externally.
Data Source
AI summary
Intent-resolution using a phrase index may include obtaining data expressing a usage intent, the data expressing the usage intent including an unresolved data portion, identifying a phrase fragment based on the data expressing the usage intent and a defined phrase pattern, the phrase fragment including the unresolved data portion, identifying, by a processor, an indexed phrase as by searching a phrase index based on the phrase fragment, wherein the indexed phrase at least partially matches the phrase fragment in accordance with the defined phrase pattern, in response to identifying the indexed phrase, obtaining a resolved request representing the data expressing the usage intent in accordance with the indexed phrase, generating a data query in accordance with the resolved request and a defined structured query language, obtaining results data responsive to execution of the data query by a database that implements the defined structured query language, and outputting the results data.