Parameterized Report Mapping for Natural Language Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in efficiently responding to natural language queries by accurately extracting and presenting relevant information from structured datasets, often requiring the construction of new parameterized reports for different views, which can be time-consuming and inefficient.
Innovation Solution
The solution involves applying linguistic parsing to natural language queries to generate language elements that are mapped to parameters of existing parameterized reports, allowing for the selection and modification of these reports to construct structured data, including visualizations, thereby reusing existing reports and augmenting the library of available reports through derivation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If new parameterized reports are constructed for different views on the dataset, then the accuracy and specificity of query responses are improved, but the time and effort required to respond to queries increases
Solution Approach 1:
The system pre-generates multiple parameterized reports with different views and parameters before queries are submitted. When a query arrives, the system selects from these pre-prepared reports rather than constructing a new one, thus maintaining accuracy while reducing response time.
Solution Approach 2:
The system modifies existing parameterized reports by changing parameters (such as date ranges, filters, aggregation levels) to create variant reports that satisfy different query requirements without building reports from scratch.
2Adaptability or versatility
If the library of parameterized reports is expanded to handle more query types, then the versatility of the system improves, but the complexity of managing and selecting reports increases
Solution Approach 1:
The system creates a unified parameterized report framework where a single report template can serve multiple query types through parameter substitution. This allows one report structure to handle diverse queries (sales, inventory, time-based analyses) without requiring separate specialized reports for each query type.
Solution Approach 2:
The system introduces an intermediary layer (the parameterized report selection mechanism) that sits between the query and the actual report generation. This intermediary matches query requirements to appropriate pre-defined report templates, simplifying the management complexity by providing a systematic approach to selecting from the report library.
3Productivity
If existing parameterized reports are reused and derived from close-match reports, then productivity and efficiency improve, but the precision and exactness of the response may be compromised
Solution Approach 1:
The system systematically modifies parameters of existing reports (such as adjusting date ranges, filtering criteria, or aggregation levels) to precisely match query requirements. This allows reuse of proven report structures while adapting them to exact query needs, maintaining both efficiency and precision.
Data Source
AI summary
A mechanism for responding to a natural language query. Linguistic parsing is applied to the natural language query to generate a set of language element(s). One or more of those language elements are mapped to parameters and/or data elements of a parameterized report, which is selected for use in responding to the query based on the mapping. The selection of the parameterized report and/or mapping to the parameterized report may use the schema of the underlying dataset that the parameterized report uses in order to perform more precise mapping and selection. The selected parameterized report is then used to construct structured data that is responsive to the query. For instance, the structured data could include visualizations. In this manner, already existing parameterized reports that were previously used to generated structured data in the form of reports, may again be used in order to respond to natural language queries.