Big Data Resource Management Platform for Access Control
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Solution Overview
Problem
Characterizing users accessing resources becomes challenging as the scale grows to big-data levels, especially when access rights are assigned on a one-to-one basis, making it difficult to manage and analyze user data effectively.
Innovation Solution
A platform that executes big data analysis techniques on access-right data to generate statistics, using machine-learning algorithms to identify user characteristics and patterns, enabling efficient management and assignment of access rights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access rights are assigned on a one-to-one basis to ensure secure resource access, then access control reliability is improved, but device complexity and difficulty of managing user data increase significantly at big-data scales
Solution Approach 1:
The patent segments user data into multiple dimensions including demographic attributes, behavioral patterns, and contextual information. This segmentation allows the system to manage big-data scale user information in organized chunks rather than as a monolithic complex structure, while maintaining one-to-one access right assignment for security.
Solution Approach 2:
The patent introduces an intermediary platform that sits between the resource management system and user data. This platform executes big data analysis techniques and machine learning algorithms to process user characteristics, thereby reducing the complexity burden on the core access control system while maintaining reliable one-to-one access right management.
2Ease of manufacture
If traditional data analysis methods are used to characterize users, then implementation simplicity is maintained, but measurement precision and accuracy of user characterization deteriorate at big-data levels
Solution Approach 1:
The patent replaces traditional mechanical data analysis methods with advanced computational approaches including machine learning algorithms and big data analytics. This substitution enables accurate user characterization at big-data scales by leveraging pattern recognition and statistical modeling capabilities that exceed traditional analytical methods.
Solution Approach 2:
The patent changes the analytical parameters from simple demographic counts to multi-dimensional user profiles incorporating behavioral patterns, contextual factors, and predictive metrics. This parameter transformation enables precise user characterization by analyzing data at multiple levels of granularity and temporal dimensions.
3Measurement precision
If detailed user data is collected to improve user characterization accuracy, then measurement precision is improved, but loss of time and computational resources increases
Solution Approach 1:
The patent implements preliminary actions by pre-processing and organizing user data into structured formats before analysis. User data is segmented and tagged with metadata in advance, creating ready-to-analyze datasets that reduce processing time during actual characterization tasks while maintaining comprehensive data collection for accuracy.
Solution Approach 2:
The patent employs periodic action through incremental data processing and batch analysis techniques. Instead of processing all user data simultaneously, the system analyzes data in periodic batches, updating user characterizations incrementally. This approach maintains high measurement precision while significantly reducing computational time and resource requirements.
Data Source
AI summary
Embodiments of the present disclosure include a platform for a resource provisioning system. The platform can execute big data analysis techniques to access-right data to generate statistics that characterize a set of users. For example, characteristics of users who access resources events can be analyzed with varying levels of detail. The access-right data can include access right assignments, and data identifying the users to which access rights are assigned. In some implementations, spatial management systems can access the platform to generate statistics for the resources.