Relational Hierarchical Key Mapping for Schema-less Data
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Solution Overview
Problem
Existing data management systems in schema-less databases face inefficiencies due to the need for multiple queries, API calls, and repetitive disk operations, leading to increased computing resource consumption and network latency, particularly in large organizations with cross-functional data sharing requirements.
Innovation Solution
A relational hierarchical key mapping system that inverts the entity-attribute relationship to attribute-key, enabling data retrieval through a single API call and single seek by aggregating attributes to a key, thus eliminating the need for multiple queries and API calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in a schema-less database with custom application layer schemas, then data flexibility and scalability are improved, but the number of API calls and computing resource consumption increase
Solution Approach 1:
The patent merges multiple scattered data points from different domains into a single aggregated data structure organized by entity keys. This consolidation allows the system to retrieve related data through a single API call rather than multiple separate calls, directly improving API call efficiency while maintaining the flexibility of schema-less storage.
Solution Approach 2:
The patent introduces an intermediary layer (the key mapping system) between the schema-less database and the application layer. This intermediary automatically aggregates data points and manages entity relationships, reducing the burden on applications to manually orchestrate multiple queries and improving overall system productivity.
2Adaptability or versatility
If data is distributed across multiple domains in a schema-less database, then data autonomy and scalability are improved, but network bandwidth usage and latency increase
Solution Approach 1:
The patent performs preliminary aggregation of data points by entity keys during data insertion and maintenance operations. By pre-organizing data in this manner, the system eliminates the need for multiple network round trips during data retrieval, reducing network bandwidth usage and latency while preserving domain autonomy.
3Loss of information
If multiple queries are performed to retrieve related data points, then data retrieval completeness is improved, but disk I/O operations and storage device wear increase
Solution Approach 1:
The patent combines multiple data points that would otherwise require separate disk I/O operations into a single aggregated data structure. This merging allows the system to retrieve complete related data in a single disk operation, reducing storage device wear while maintaining data retrieval completeness.
Data Source
AI summary
Some aspects of the present technology relate to technologies for context and knowledge sharing abstraction across entities using a relational hierarchical key mapping system. In accordance with some configurations, a first set of one or more data points for a first set of one or more keys is received at a first domain and stored in a schema-less data store. A second set of one or more data points for a second set of one or more keys is received at a second domain and stored in the schema-less data store. Based on at least one shared key, the first set of one or more data points and the second set of one or more data points are linked as a virtual entity such that a request to retrieve data points of the one or more data points is performed as a single seek and a single API call.