Hierarchical Data Flattening for Relational Schema Conversion
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Solution Overview
Problem
Providers of virtualization software face significant challenges in collecting and analyzing data from various software components due to differing data schemas, requiring substantial human and computational effort to prepare the data for meaningful analysis.
Innovation Solution
A method is introduced to generate telemetry data by serializing application programming interface (API) data from client computers and converting it into a relational schema using JavaScript Object Notation for Linked Data (JSON-LD) serialization, followed by data transformation into a flat relational schema for efficient storage and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is collected from multiple software components with different schemas, then data collection coverage is improved, but data processing complexity increases
Solution Approach 1:
The patent introduces an intermediary data transformation layer that mediates between diverse software component data schemas and the target relational database schema. This intermediary layer automatically maps and transforms data from various component schemas into a unified relational structure, eliminating the need for complex manual processing while maintaining broad data collection coverage across multiple components.
2Adaptability or versatility
If hierarchical data structure is used to represent parent-child relationships, then data modeling flexibility is improved, but database query complexity increases
Solution Approach 1:
The patent segments the hierarchical data structure into separate relational database tables, with parent and child entities represented as distinct tables connected through foreign key relationships. This segmentation transforms complex nested hierarchical queries into simpler relational queries while preserving the parent-child relationships through normalized database design, thereby reducing query complexity while maintaining data modeling flexibility.
Data Source
AI summary
An example method of generating telemetry data from application programming interface (API) data in a computing system includes: obtaining the API data from a plurality of software components executing in a plurality of client computers; serializing the API data for transmission from the client computers to a server computer, the serialized data including a parent object having a child object; and generating the telemetry data having a relational schema from the serialized telemetry data by generating a table for the parent object and storing attributes of the child object in the table generated for the parent object.