Dataset Join Operations for Inconsistent Dimensions
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Solution Overview
Problem
Existing database management systems fail to perform JOIN operations over datasets with inconsistent dimensions, leading to errors and inconsistencies, despite potential usefulness of aggregate data from such operations.
Innovation Solution
A frontend server translates JOIN operations into formal structured database JOINs based on metadata indicating compatibility between columns, ignoring compatible dimensions to form implicit JOIN commands over common columns, thus enabling JOIN operations over datasets with inconsistent dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the JOIN command requires consistent dimensions (all datasets must include a common column), then the database syntax strictness and data consistency are improved, but the ability to perform useful aggregate data operations across datasets with different dimensions is lost
Solution Approach 1:
The patent introduces an intermediary mechanism (the JOIN command with dimension compatibility checking) that mediates between datasets with different dimensions. The system checks if dimensions are compatible (e.g., both are temporal or both are categorical) rather than requiring exact matching, allowing the JOIN operation to proceed while maintaining data integrity through the compatibility verification process
2Reliability
If the JOIN command fails on inconsistent dimensions, then the syntax strictness and error prevention are improved, but the productivity and ease of operation are worsened due to manual intervention requirements
Solution Approach 1:
The system performs self-service by automatically checking dimension compatibility and executing JOIN operations without requiring manual intervention. The database management system itself evaluates whether dimensions are compatible and proceeds with the operation autonomously, eliminating the need for users to manually pre-process or transform datasets to match dimensions exactly
3Measurement precision
If manual intervention is required for JOIN operations on datasets with inconsistent dimensions, then the precision and control are improved, but the ease of operation and automation extent are worsened
Solution Approach 1:
The system applies partial checking by not requiring complete dimension matching between all datasets, but only checking for compatibility of the specific dimensions involved in the JOIN operation. This partial verification approach maintains sufficient precision for data integrity while allowing operations to proceed without excessive manual intervention
Data Source
AI summary
A database system comprising an interface and a processor may perform techniques described in this disclosure. The interface may receive a query for accessing a first dataset and a second dataset, where the query identifies multiple dimensions of the first dataset and the second dataset. The first dataset may include a first dimension, and the second dataset may include a second dimension, but excludes the first dimension. The processor may translate the query into a structured query that conforms to a database query language, where the structured query defines a join between the first dataset and the second dataset over the second dimension. The processor may also transmit the structured query to a database, and receive, responsive to the structured query, an indication that a virtual table was created that combines the first dataset and the second dataset over the second dimension of the second dataset.