Semantic Graph Query Troubleshooting Visualization
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Solution Overview
Problem
Traditional database search systems face challenges in troubleshooting search queries due to their reliance on unwieldy data structures, making it difficult to identify faults and inefficient to analyze or search data, as queries often need to be constructed on a case-by-case basis and require manual review of multiple data sources, consuming additional network and processing resources.
Innovation Solution
The implementation of a semantic data structure, such as a semantic graph, that overlays and organizes disparate traditional data sources within a semantic model, allowing for automatic analysis and visualization of query faults by mapping semantic classes and attributes to traditional data sources, enabling quick identification of problematic areas within the query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional data structures (tables) are used to store data, then data collection is easy and efficient use of physical data carriers is achieved, but data analysis and search operations become cumbersome and time-consuming
Solution Approach 1:
The patent introduces an intermediary layer (semantic model, graph database, or data warehouse) between the traditional tabular data sources and the search query interface. This intermediary pre-processes and organizes data from multiple tables into a unified structure with defined relationships, allowing complex queries to be executed efficiently without directly accessing the underlying fragmented tables.
Solution Approach 2:
The system performs preliminary actions by pre-computing and pre-organizing data relationships in the intermediary layer during data loading or periodic updates. This includes establishing entity relationships, creating indexes, and preparing aggregated views beforehand, so that when search queries are executed, the system can quickly retrieve results without performing complex real-time joins across multiple tables.
2Reliability
If manual review of multiple data sources is performed to identify query faults, then problem identification is possible, but the process is cumbersome and time-consuming
Solution Approach 1:
The system implements feedback mechanisms that automatically monitor query execution across the intermediary layer and provide real-time diagnostics. When a query fault occurs, the system analyzes the query path through the semantic model or graph structure, identifies which data sources or relationships are causing issues, and returns specific feedback to the user about the problem location and nature, eliminating the need for manual troubleshooting.
Solution Approach 2:
The troubleshooting system performs self-service by automatically detecting, analyzing, and diagnosing query faults without human intervention. The intermediary layer includes built-in validation rules, consistency checks, and error detection algorithms that autonomously identify problematic queries, data source issues, or relationship errors and provide corrective suggestions.
3Reliability
If multiple smaller queries are created sequentially to identify problematic data structures, then the source of the problem can be located, but additional network and processing resources are consumed
Solution Approach 1:
Instead of creating multiple smaller queries to progressively narrow down the problem, the system performs a single comprehensive analysis of the entire query path through the intermediary layer. It evaluates all data sources, relationships, and transformation steps involved in the original query simultaneously, identifying the problematic area in one operation rather than through sequential trial-and-error with multiple partial queries.
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.