Data Set Membership Using Relative Value and Access Tracing
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Solution Overview
Problem
Current data set generation and delivery methods are manual, costly, and sub-optimal, failing to consider data relevance, security, and compliance, leading to inefficient data set creation and increased processing times.
Innovation Solution
Implementing a Relative Value System (RVS) with a metadata control (MC) plane and data governance control (DGC) plane to automate data set generation, prioritize relevant records, and ensure security and compliance, using a learning paradigm to optimize data set creation based on user feedback and past utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data set generation methods are used, then data set creation can be performed with simple processes, but processing times increase and productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-calculating relative values for data records and pre-organizing data sets based on anticipated user needs. The metadata control plane and data governance control plane prepare data in advance, so when a user requests a data set, the system can quickly assemble and deliver relevant data without extensive processing time.
Solution Approach 2:
The patent replaces manual mechanical data set generation processes with an automated intelligent system. The system uses algorithms to automatically determine data set membership, calculate relative values, and deliver data sets without human intervention. This substitution of mechanical manual processes with automated computational processes dramatically increases productivity and reduces processing time.
2Reliability
If current data set delivery methods are used, then implementation is straightforward, but relevance and value of delivered data sets are insufficient
Solution Approach 1:
The system implements feedback mechanisms where user interactions with data sets are tracked and used to improve future data set deliveries. The system learns from user behavior patterns and adjusts the selection and prioritization of data records accordingly. This feedback loop continuously improves data relevance without requiring proportional increases in system complexity.
Solution Approach 2:
The system changes parameters by dynamically adjusting the relative values assigned to data records based on multiple factors including user preferences, data freshness, source reliability, and usage patterns. These parameter changes enable the system to adaptively prioritize relevant data while managing complexity through standardized evaluation criteria.
3Reliability
If comprehensive security and compliance checks are implemented, then data protection is improved, but processing complexity and time increase
Solution Approach 1:
The system performs security and compliance checks as preliminary actions during data set assembly. The data governance control plane evaluates data records against security policies and compliance requirements before inclusion in delivered data sets. By performing these checks in advance rather than as a separate post-processing step, the system ensures security and compliance without significantly increasing overall processing complexity.
4Productivity
If automated data set generation is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The automated system is segmented into distinct functional planes: the metadata control plane responsible for data discovery and valuation, and the data governance control plane responsible for security and compliance. This segmentation allows each component to specialize in specific tasks, improving overall productivity while managing complexity through modular design and clear separation of concerns.
Data Source
AI summary
One example method includes receiving a user request for a data set, and the user request includes information concerning user requirements for the data set, identifying data records that satisfy one or more of the user requirements, calculating a respective relative value for each of the data records, and the relative values are based in part on the user requirements, and providing access controls for each data record that enable tracing of accesses of the data record.