DAG Data Structure with Time-of-Life Intervals
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Solution Overview
Problem
Traditional databases face inflexibility and inefficiency in storing and managing real-world data, as they often require rigid data structures that cannot accommodate changing information or specific company-related data without global schema changes, leading to inconsistencies and unintended errors.
Innovation Solution
A directed acyclic graph (DAG) data structure is used, where data items are represented as nodes with time-of-life intervals, allowing for flexible storage and manipulation of data by splitting nodes for updates, and using edge types to model relationships, with indexing and permission control for secure access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional database schema is used, then data structure is well-defined and consistent, but flexibility to adapt to changing information needs is lost
Solution Approach 1:
The patent implements dynamic data structures where the schema is not fixed but can evolve over time. Data items have time-of-life intervals that allow them to be added, modified, or removed without requiring global schema changes. This enables the database to adapt its structure dynamically based on changing information needs while maintaining consistency through version control and temporal validation.
Solution Approach 2:
The patent segments the database schema into individual data items with independent time-of-life intervals rather than requiring global schema definitions. Each data item can be independently added, modified, or removed within its valid time period, allowing localized changes without affecting the entire database structure. This segmentation enables flexible adaptation while maintaining overall system consistency.
2Adaptability or versatility
If fields are added to traditional database, then new information can be stored, but global schema change is required affecting all records
Solution Approach 1:
The patent segments the database into independent data items that can be individually added without requiring global schema changes. When new information fields are needed, they can be added as new data items with specific time-of-life intervals, affecting only the relevant records rather than requiring updates to all database records. This segmentation dramatically improves the efficiency of adding new fields.
Solution Approach 2:
The patent allows data items to be added with future effective dates, enabling preliminary action where new information fields can be prepared in advance and automatically become active at specified times. This eliminates the need for immediate global schema changes and allows for more efficient, planned additions of new fields.
3Loss of information
If data is removed from traditional database, then obsolete information is deleted, but risk of inconsistency remains with related information
Solution Approach 1:
The patent implements dynamic data retention where obsolete information is not immediately deleted but is automatically retired when its time-of-life interval expires. Related information maintains references to the retired data items, which remain accessible in a retired state. This dynamic approach ensures database consistency by preventing orphaned references while still removing obsolete information from active use.
Solution Approach 2:
The patent discards obsolete information by retiring data items when their time-of-life intervals expire, but recovers the ability to access this information when needed. Retired data items can be reactivated or referenced by new data items, ensuring that obsolete information is properly managed without creating consistency issues. This approach maintains reliability while enabling efficient removal of obsolete data.
4Reliability
If non-valid data is marked as deleted in traditional database, then data integrity is maintained, but risk of accessing non-valid data remains
Solution Approach 1:
The patent implements dynamic validation where data items automatically transition from valid to retired status when their time-of-life intervals expire. The system dynamically tracks the validity status of each data item and enforces access controls based on current validity. This eliminates the risk of accessing non-valid data because the system automatically prevents access to retired items unless explicitly reactivated, while maintaining data integrity through structured retirement processes.
Data Source
AI summary
A data structure for storing items of information having a time of life includes a time of life interval for each item of information, and methods for using the same. The items of information are organized in a data structure having nodes and edges connecting the nodes. This data structure is preferably a directed acyclic graph structure. The data structure includes parent nodes and child nodes. The time of life interval specified for any child node generally is contained within the time interval specified for that child node's parent node, such that the data structure includes no child nodes with a time of life interval that falls outside of the time of life interval of its parent node.