Curated Portable Datamart with Virtualized Secure Data Access
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Solution Overview
Problem
Traditional data curation approaches are costly, complex, and lack the ability to efficiently integrate client-specific data and reporting, often requiring large centralized systems that are not secure or portable.
Innovation Solution
A curated portable datamart system that includes a virtual raw datamart, a semantic layer, a security and client context layer, and an API, which allows for client-specific rule definition, secure access to individual-specific data, and generation of curated data deliverables on demand, ensuring data portability and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional centralized enterprise warehouses are used for data curation, then data quality and security can be ensured, but the system becomes costly and complex
Solution Approach 1:
The patent segments the centralized enterprise warehouse into multiple virtual datamarts, each serving specific clients or purposes. This segmentation allows data to be organized into manageable, purpose-specific units while maintaining overall system integrity and security standards.
Solution Approach 2:
The patent introduces a virtualization dimension by creating virtual datamarts that overlay the physical data infrastructure. This allows multiple logical data environments to coexist on shared physical resources, reducing complexity while maintaining data quality through virtual enforcement of security and quality rules.
2Reliability
If traditional centralized enterprise warehouses are used for data curation, then data security can be ensured, but operational costs increase
Solution Approach 1:
The patent creates a universal data curation platform that serves multiple clients and purposes through virtual datamarts. This multi-functional approach allows a single infrastructure to handle diverse data curation needs, reducing operational costs while maintaining security through unified policy enforcement.
Solution Approach 2:
The patent uses virtual copies of data and data environments rather than requiring separate physical infrastructure for each client. Virtual datamarts create isolated, secure data environments through software-based copying and abstraction, reducing hardware costs while maintaining security boundaries.
3Reliability
If traditional centralized systems are used, then data quality can be maintained, but the ability to integrate client-specific data and reporting is limited
Solution Approach 1:
The patent applies local quality by allowing each virtual datamart to have customized data quality rules, security policies, and reporting configurations tailored to specific client needs. This enables client-specific integration while maintaining overall data quality through localized control mechanisms.
Solution Approach 2:
The patent introduces dynamic configurability to the data curation system, allowing virtual datamarts to be created, modified, and customized based on changing client requirements. This dynamic approach enables flexible integration of client-specific data and reporting while maintaining data quality through enforceable rules.
4Reliability
If large centralized systems are deployed, then data security can be ensured, but portability and flexibility are reduced
Solution Approach 1:
The patent extracts the security and data quality enforcement mechanisms from the physical infrastructure and embeds them in the virtualization layer. This allows secure data environments to be packaged and moved as virtual entities, improving portability while maintaining security through embedded policy enforcement.
Data Source
AI summary
A curated portable datamart system, method, and apparatus are provided. The method, implemented by one or more processors, comprises: inputting, via an application programming interface (API), a client query of a client, the client query requesting a temporal output able to be calculated by individual-specific raw data identifiable within the raw data of a virtual raw datamart; invoking a security and client context layer to authorize the client to access the individual-specific raw data that is specific to the client query; responsive to authorizing the client query, invoking a semantic layer to retrieve the individual-specific raw data from the virtual raw datamart, and curate the individual-specific raw data to generate a curated data deliverable, the curated data deliverable comprising temporal data generated from one or more raw data items accessed by the semantic layer from the virtual raw datamart; and outputting, via the API, the curated data deliverable.