Virtual Schema Generation for Non-Relational Database Analysis
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Solution Overview
Problem
Non-relational databases lack a unifying schema, making it difficult to analyze and understand the data structures within them, especially in 'big data' scenarios where relational databases become impractical.
Innovation Solution
A system that generates a virtual schema based on sampled data, providing a probabilistic model of the database architecture, allowing for efficient analysis and visualization of data structures even in dynamic schema settings, enabling users to infer properties of the entire database from sampled portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If non-relational databases are used to store big data, then the database capacity and flexibility are improved, but the difficulty of analyzing data structures increases
Solution Approach 1:
The patent introduces an intermediary component (virtual schema generator/analyser) that sits between the non-relational database and the user/query system. This intermediary automatically generates virtual schemas from sampled database contents, translating the unstructured data into a comprehensible format without requiring users to directly analyze the raw non-relational database structure.
Solution Approach 2:
The system creates a copy or representation of the database structure in the form of a virtual schema. Instead of requiring users to directly examine and understand the complex non-relational database structure, the system generates a simplified schema copy that mirrors the essential data relationships and structures, making analysis tractable.
2Measurement precision
If the entire database is analyzed to derive a data model, then the accuracy of the data model is improved, but the computational cost becomes prohibitive
Solution Approach 1:
The patent applies partial action by analyzing only a representative sample of the database rather than the entire dataset. The system selects and analyzes a subset of database contents that are sufficient to derive an accurate virtual schema, achieving acceptable model accuracy with significantly reduced computational resources compared to analyzing the complete database.
Solution Approach 2:
The system segments the database analysis task into manageable portions by working with sampled data subsets. Instead of processing the entire database at once, the analysis is divided into smaller, independent sampling and analysis operations that can be performed efficiently and scaled as needed.
Data Source
AI summary
One aspect relates to generating a unifying virtual schema in a non-relational database. A database is analyzed either in its entirety, or in part by using a sample of the database as a representative portion of the database as a whole. Common attributes within the database may be identified, ranked and displayed to a user through one or more of various visualizations. In other examples, the user may further refine the way the data is displayed and how much is displayed by interacting with a user interface.