Autonomous Logical Model Inconsistency Detection
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Solution Overview
Problem
Conventional methods for testing logical models for inconsistencies in database schemas are cumbersome and require significant manual effort, often leading to undetected flaws and performance issues due to the complexity of logical models and their interactions with underlying data schemas.
Innovation Solution
The system autonomously generates and executes pairs of logical queries based on metadata to identify inconsistencies by comparing query results, translating logical queries into database queries that target fact and dimension tables, and analyzing the results to detect alignment issues between the logical model and the data schema.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional manual testing methods are used to test logical models for inconsistencies, then testing can be performed with simple tools, but the testing process becomes cumbersome and requires significant manual effort
Solution Approach 1:
The system performs self-testing by automatically generating test queries, executing them against the logical model, and identifying inconsistencies without requiring manual intervention. The autonomous tester generates pairs of logical queries that target the same logical object, executes both queries, and automatically compares results to detect inconsistencies in the logical model or database schema.
Solution Approach 2:
The system performs preliminary actions by automatically generating comprehensive test query pairs before actual testing begins. The autonomous tester pre-generates multiple pairs of logical queries that target various logical objects in the logical model, ensuring thorough coverage of potential inconsistencies before execution.
2Reliability
If manual testing of logical models is performed, then resource consumption is low, but inconsistencies and flaws remain undetected due to complexity
Solution Approach 1:
The autonomous tester acts as an intermediary between the logical model and the testing process. It automatically generates test queries, executes them, and analyzes results to identify inconsistencies. The system translates logical queries into database-specific queries and manages the comparison process, serving as a mediator that handles the complexity of testing while providing reliable inconsistency detection.
3Productivity
If automated query generation is implemented, then testing efficiency improves, but the complexity of generating and managing query pairs increases
Solution Approach 1:
The testing process is segmented into distinct components: query pair generation, query execution, and result comparison. The autonomous tester divides the complex testing task into manageable segments, generating pairs of logical queries that target specific logical objects, executing each pair separately, and comparing results independently. This segmentation reduces the perceived complexity while maintaining high productivity.
4Manufacturing precision
If comprehensive inconsistency detection is performed, then model quality improves, but manual effort and time investment increase significantly
Solution Approach 1:
The system replaces manual mechanical testing processes with automated computational processes. Instead of manually designing and executing test queries, the autonomous tester automatically generates logical query pairs, executes them against the logical model and database, and analyzes results using computational methods. This substitution of manual mechanics with automated systems achieves comprehensive inconsistency detection while reducing time investment.
Data Source
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AI summary
Embodiments autonomously test a logical model for inconsistencies. For example, metadata descriptive of a logical model can be received, where the logical model includes an abstraction for a database schema, the database schema is implemented at a database, and the database schema includes a fact table and a dimension table. Logical queries can be automatically generated including at least first and second logical queries based on the retrieved metadata, where the first and second logical queries target a logical object of the logical model. At least the first and second logical queries can be issued to a server that hosts the logical model, where, at the server, the first and second logical queries are translated to first and second database queries, and the first and second database queries target at least a fact table and a dimension table from the database schema. Query results received from execution of the first and second database queries can be compared. Inconsistencies can be identified when the comparison of the query results does not meet a criterion.