Graphical Query Representation for Automated Data Insight Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing and querying multi-domain data sets within organizations is challenging due to the difficulty in finding, connecting, and joining dispersed data sets, and the need for domain expert guidance, which is often limited, leading to inaccurate and incomplete data insights.
Innovation Solution
Graph-based query processing techniques generate graphical representations of queries, leveraging historical queries to automate query completion, correction, and enrichment, using generative graph models and prediction algorithms to suggest modifications and corrections, thereby improving query composition without relying on domain experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual query composition is used, then domain expertise can guide accurate queries, but the process is time-consuming and requires limited domain expert availability
Solution Approach 1:
The system performs preliminary actions by generating graphical representations of queries in advance and using prediction algorithms to identify potential errors and suggestions before the user finalizes the query. This allows automated preparation and pre-processing of query improvements, reducing the time users need to spend on query composition while maintaining accuracy through pre-computed suggestions.
Solution Approach 2:
The patent introduces an intermediary system consisting of graphical query representations and prediction algorithms that mediate between the user's initial query and the final optimized query. This intermediary automatically analyzes query structure, identifies errors, and provides corrections without requiring direct domain expert intervention, thus reducing time loss while preserving query accuracy.
2Measurement precision
If domain experts are used to guide query composition, then accurate data insights can be obtained, but the limited availability of domain experts restricts productivity
Solution Approach 1:
The system enables self-service by automatically analyzing queries using prediction algorithms and graphical representations, allowing the query system to improve itself without external domain expert intervention. The prediction algorithms autonomously identify errors, suggest corrections, and optimize query composition, thereby maintaining data insight accuracy while dramatically increasing productivity through automated self-improvement capabilities.
Solution Approach 2:
The patent replaces the mechanical system of manual domain expert review with an automated computational system using prediction algorithms and graphical query analysis. This substitution eliminates the bottleneck of limited expert availability while maintaining or improving query accuracy through systematic automated analysis of query structure and data relationships.
3Loss of information
If complex multi-domain data sets are queried, then comprehensive data insights can be obtained, but the difficulty in finding and connecting dispersed data sets increases
Solution Approach 1:
The patent segments complex multi-domain queries into graphical representations where different data sets and their relationships are visually separated into distinct nodes and edges. This segmentation makes it easier to identify, connect, and manage dispersed data sets by breaking down the complex query structure into manageable components that can be individually analyzed and connected through the graphical interface.
Solution Approach 2:
The system transforms complex query structures from traditional linear or tabular formats into a graphical dimension, adding a visual spatial dimension to query representation. This dimensional change allows users to better perceive connections between dispersed data sets across multiple domains, making it easier to identify missing links and ensure data completeness while managing query complexity.
Data Source
AI summary
Techniques are provided for automated query modification using graphical query representations. One method comprises obtaining a query referencing multiple fields in one or more information elements; generating a graph of the query by: establishing nodes for referenced fields; connecting the established nodes corresponding to referenced fields from a same information element using one or more edges; adding edges, for operations in the query that establish a connection between information elements based at least in part on a related field in the information elements, to connect the nodes corresponding to the related fields; and setting a status of nodes corresponding to fields that are selected in the query to a selected status; and initiating an automatic generation of a modification of a portion of the query based at least in part on the graph. The modification may comprise a correction, completion and/or enrichment of the query.