Natural Language Dashboard Generation for Data Storage Systems
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Solution Overview
Problem
Natural language search applications are limited in generating and applying domain-specific language requests that involve operations beyond search queries, such as alert commands, making it difficult for users unfamiliar with specific domain-specific languages to access and analyze data from various data sources effectively.
Innovation Solution
A method is developed to generate a dashboard by translating natural language requests into appropriate domain-specific language requests, including search queries, alert commands, and dashboard commands, and transmitting these commands to the data storage system to generate visual graphics and alerts, thereby enabling users to interact with diverse data sources without expertise in specific languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language search applications are used to enable users to access data from diverse data sources, then ease of operation is improved, but the application cannot properly generate and apply DSL requests that involve operations other than search queries
Solution Approach 1:
The translation engine is enhanced to provide universal translation capabilities across multiple DSL domains (search queries, alerts, dashboards, and other operational commands). Instead of being limited to search query translation only, the system now translates natural language requests into various DSL request types, making the NL search application multi-functional and adaptable to different data source operations.
Solution Approach 2:
The translation engine acts as an intermediary layer between natural language input and domain-specific language output. This mediator component translates NL requests into appropriate DSL requests for different data source types and operations, enabling users to interact with diverse data sources using natural language without needing to learn multiple DSLs.
2Device complexity
If the translation functionality in NL search applications is limited to basic search queries, then device complexity is reduced, but the application cannot provide users with the same opportunities that DSL provides to skilled users
Solution Approach 1:
The translation engine is designed with dynamic capabilities to adapt its translation functionality based on the type of DSL request needed. The system can dynamically generate different types of DSL requests (search queries, alerts, dashboards, etc.) based on the natural language input, allowing it to provide comprehensive functionality while maintaining a unified translation interface that doesn't increase apparent complexity for users.
3Adaptability or versatility
If comprehensive translation capabilities are added to handle various DSL operations, then adaptability is improved, but processing time and complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and categorizing natural language requests to determine the appropriate DSL request type before actual translation occurs. This preliminary classification enables the translation engine to efficiently route requests to the appropriate translation logic, reducing overall processing time while maintaining comprehensive adaptability across different DSL operations.
Data Source
AI summary
In various embodiments, a natural language (NL) application enables users to more effectively access various data storage systems based on NL requests. The NL application includes functionality for selecting an optimal interpretation algorithm, generating a dashboard, and/or generating an alert based on an NL request. Advantageously, the operations performed by the NL application reduce the amount of time and user effort associated with accessing data storage systems and increase the likelihood of properly addressing NL requests.