Access Data Storage Management via Metadata Mapping
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Solution Overview
Problem
The rapid growth of access data in computing systems poses challenges in storage management, as unbounded storage is not feasible due to economic and regulatory constraints, requiring effective methods to limit storage without compromising security or compliance.
Innovation Solution
Implementing a metadata groups structure, access data boxes structure, and a mapping structure with a capacity usage policy to manage access data storage, allowing or denying placement based on available capacity and policy requirements, thereby controlling storage costs and compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access data is stored without limits, then complete access history is preserved for security analysis, but storage costs increase unboundedly and regulatory requirements cannot be met
Solution Approach 1:
The patent segments access data into different retention periods and storage locations. Recently accessed data is kept in primary storage with full detail, while older data is moved to archive storage with reduced detail or aggregated form. This segmentation allows the system to maintain security analysis capability for recent data while limiting long-term storage costs through selective retention and compression of historical data.
2Quantity of substance
If access data storage is limited to control costs, then storage costs are reduced, but complete access history may be lost affecting security investigations
Solution Approach 1:
The patent applies local quality by differentiating storage requirements based on data characteristics and age. Recent access data is stored with full detail in high-capacity storage, while older data is stored in archive storage with reduced detail or aggregated statistics. This allows cost-effective storage limits while preserving critical security information where it matters most - in recent access patterns.
Solution Approach 2:
The system changes parameters of data representation over time. Recently accessed data maintains full resolution with complete metadata, while older data undergoes parameter changes including aggregation, summarization, or compression. This parameter transformation reduces storage requirements for historical data while retaining sufficient information for security analysis of access patterns.
3Reliability
If different retention policies are applied to different data types, then regulatory compliance is improved, but storage management complexity increases
Solution Approach 1:
The patent implements self-service through automated policies that classify and route access data based on predefined criteria. The system automatically determines retention periods, storage locations, and compression levels without manual intervention. This automation handles the complexity of multiple retention policies internally, providing regulatory compliance while keeping the user interface simple and management overhead low.
Data Source
AI summary
Some embodiments manage storage of access data to provide flexible and granular control over storage costs without risking policy compliance, regulatory compliance, or data breach investigation. Resources are classified and given metadata labels. Resource access data is associated with the accessed resource metadata label. A mapping is defined between metadata groups and access data storage boxes. Access data storage box definitions may specify metadata labels. A mapping structure also defines a policy governing use of available storage capacity in access data storage boxes. Per the policy and the available capacity, particular access data may be stored in a particular box, be spilled over to a different box, or be denied storage. Accordingly, the costs of storing access data can be capped and made predictable, and storage of specific kinds of access data can be favored.