Query Visualization Using Semantic Models to Isolate Faulty Attributes
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Solution Overview
Problem
Traditional data structures in search systems require cumbersome and resource-intensive manual troubleshooting of search queries due to their reliance on specific data sources, making it difficult to identify query faults and inefficient to analyze or search data.
Innovation Solution
A semantic data structure is used to overlay and organize disparate traditional data sources, allowing for automatic analysis and visualization of query faults by computing statistic scores and highlighting problematic parts of the query using a visual representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of data structures is performed to identify query faults, then query troubleshooting can be conducted, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The patent replaces manual mechanical review processes with automated computational analysis. The system automatically analyzes query execution plans, data source statistics, and join operations to identify faults, eliminating the need for manual inspection of data structures and significantly reducing troubleshooting time while maintaining or improving identification accuracy.
Solution Approach 2:
The system enables self-service query troubleshooting by automatically diagnosing issues without requiring manual intervention. The automated analysis engine independently examines query performance, identifies problematic data sources or operations, and provides diagnostic information, allowing the system to serve itself in the troubleshooting process.
2Difficulty of detecting and measuring
If smaller queries are manually created to identify problematic data structures, then the source of query faults can be located, but network and processing resources are unnecessarily consumed
Solution Approach 1:
Instead of creating multiple smaller queries to isolate problems, the system performs a single comprehensive analysis that examines all relevant data sources and query components simultaneously. This approach uses partial information (statistics and execution plans) to achieve complete diagnostic coverage without the resource cost of multiple sequential query executions.
Solution Approach 2:
The system performs preliminary analysis of data source statistics and query execution plans before actual query execution or troubleshooting is needed. By pre-computing and storing diagnostic information about data sources, the system can quickly identify problematic areas without requiring resource-intensive runtime analysis or multiple test queries.
3Quantity of substance
If traditional data structures are used to store data, then data can be efficiently stored on physical carriers, but search and analysis operations become difficult and cumbersome
Solution Approach 1:
The patent introduces an intermediary layer between traditional data structures and search operations. This layer includes automated query analysis tools, execution plan generators, and diagnostic systems that mediate between the user's search intent and the underlying data structures, making operations easier without changing the efficient storage format.
Solution Approach 2:
The system segments the complex search and analysis operation into distinct analytical components: query parsing, execution plan generation, data source statistics analysis, and fault identification. This segmentation allows each component to be optimized independently, improving ease of operation while maintaining efficient data storage.
Data Source
AI summary
Systems and methods are disclosed for creating a query visualization. A system receives an input of a query, with semantic classes, attribute and links for the semantic classes, wherein the semantic model data structure maps the at least one attribute a source data structure. The system generates for display a visual representation of the query. The system accesses the at least one source data structure mapped to the at least one attribute to compute at least one statistic score based on calculating a number of records in the at least one source data structure comprising a value for the at least one attribute. The system then modifies the visual representation of the query based on identifying a problematic attribute in the query based on the at least one statistic score.