Knowledge Graph Version Nodes for Draft and Published Data Assets
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Solution Overview
Problem
Existing systems lack a robust mechanism for managing and auditing versions of data assets, particularly in enterprise environments, leading to potential inaccuracies and difficulties in tracking changes and ensuring that only approved, published versions are accessible.
Innovation Solution
A knowledge graph is used to store data assets, where each version is represented by a node, with status indicators determining whether a version is a draft or published. Draft versions can be edited, while published versions are immutable, and the system maintains a history of previous versions for auditing and tracking changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data assets are stored in a knowledge graph with version nodes and status indicators, then data integrity and auditability are improved, but system complexity increases
Solution Approach 1:
The data asset is segmented into multiple version nodes (e.g., version node 204, 208, 212) connected to a root node, with each version node representing a distinct state of the data asset. This segmentation allows tracking of individual versions while maintaining overall data integrity through the structured graph relationship.
Solution Approach 2:
Status indicators serve as intermediaries between version nodes and users, automatically managing the editable/published state transitions. These indicators mediate access control and audit tracking without requiring complex manual management, thus improving reliability while keeping the system manageable.
2Ease of operation
If draft versions of data assets are made editable by users, then ease of operation is improved, but risk of unauthorized changes increases
Solution Approach 1:
Different access permissions are applied locally to different version nodes based on their status. Draft version nodes allow editing by authorized users, while published version nodes prevent editing. This local differentiation of quality (editability) enables easy operation for drafts while protecting published versions from unauthorized changes.
Solution Approach 2:
The system provides feedback through status indicators that clearly show whether a version is in draft or published state. This feedback mechanism guides user behavior, allowing editing only when appropriate (draft state) and preventing unauthorized changes to published versions, thus balancing ease of operation with security.
3Reliability
If published versions of data assets are made immutable, then data reliability is improved, but flexibility for updates decreases
Solution Approach 1:
The system creates a new draft version node before publishing updates, allowing all necessary modifications to be prepared and reviewed in advance. Once the draft is approved and published, the new version becomes the immutable published version. This preliminary action in draft state enables flexibility for updates while ensuring reliability through the immutability of published versions.
Solution Approach 2:
The version nodes dynamically transition between draft and published states. In draft state, nodes are editable to accommodate updates and changes. Upon publication, nodes become immutable to ensure reliability. This dynamic state change allows the system to be flexible during development and rigid during deployment, resolving the contradiction between adaptability and reliability.
4Measurement precision
If a complete audit trail of all version changes is maintained, then measurement precision is improved, but loss of time for data management increases
Solution Approach 1:
The knowledge graph creates a structural copy of the data asset's evolution through version nodes, where each node represents a snapshot of the data at a particular state. This copying approach provides a complete audit trail with precise measurement of changes without requiring continuous manual tracking, thus improving audit precision while reducing time loss through automated version capture.
Data Source
AI summary
A method includes storing data in a knowledge graph, defined by nodes connected to edges, in a database. A root node representing a data asset of the knowledge graph is connected to a first version node by a first edge, the first version node representing a first version of the data asset, and being associated with a status indicator having a first state indicating that a version of the data asset represented by the version node is an editable draft version of the data asset, and the corresponding version of the data asset is editable by a user of the knowledge graph. The method includes, by one or more processors, changing the state of the status indicator associated with the first version node to a second state indicating that a version of the data asset represented by the version node is a published version of the data asset.