Semantically-Identified Data Lifetime Management via Graph Structure
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Solution Overview
Problem
Managing the lifetime of semantically-identified data across software systems is complex due to inconsistencies in representing and enforcing time-to-live (TTL) information, especially when data is shared between systems with differing granularities and conventions.
Innovation Solution
A method and system that utilize a graph data structure to store semantically-identified data items with embedded lifetime information, allowing for consistent management and enforcement of TTL, including monitoring and updating data items when they expire, and notifying systems of changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If lifetime information is shared between software systems using conventions or standards, then interoperability is improved, but the complexity of managing granularity mismatches and enforcement increases
Solution Approach 1:
The patent introduces an intermediary layer (the data sharing service with graph data structure) that mediates between software systems. This intermediary automatically handles lifetime information storage, monitoring, and enforcement, eliminating the need for systems to directly manage granularity mismatches and complexity while maintaining interoperability through standardized interfaces.
Solution Approach 2:
The system enables self-service by embedding lifetime information directly with data items in the graph data structure. The data sharing service automatically monitors and enforces lifetime without requiring external systems to implement complex lifetime management logic, allowing systems to simply consume and produce data with embedded lifetime metadata.
2Loss of information
If lifetime information is embedded directly with data items, then discoverability and maintainability are improved, but the storage and processing overhead increases
Solution Approach 1:
The patent merges lifetime information with data items by storing them together in the graph data structure. This combination improves discoverability as lifetime information travels with the data it describes, while the overhead is managed efficiently through the graph structure's ability to store metadata alongside core data without significant performance penalty.
3Reliability
If software systems manually enforce lifetime and take action when it expires, then control over data validity is improved, but the sophistication and coding complexity required increases
Solution Approach 1:
The data sharing service performs self-service by automatically monitoring lifetime information and enforcing validity constraints. The system autonomously determines when data expires and takes appropriate actions without requiring consuming software systems to implement complex lifetime enforcement logic, thereby maintaining reliability while reducing coding complexity.
Solution Approach 2:
The system implements feedback mechanisms where the data sharing service continuously monitors lifetime information and provides automatic notifications or actions when data expires. This feedback loop ensures data validity control is maintained while eliminating the need for external systems to manually check and enforce lifetime constraints.
Data Source
AI summary
A method and system for managing the lifetime of semantically-identified data is provided. A graph data structure is maintained in storage of a computing device. The graph data structure has data items and relationships therebetween that are semantically identified. Lifetime information is stored in the storage. The lifetime information describes the period during which at least one of the data items in the graph data structure is valid.