Adaptive Data Retrieval via Predictive Authorization Batching
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Solution Overview
Problem
In security-conscious environments where databases are distributed and access policies change frequently, efficiently delivering data while ensuring runtime authorization is challenging, especially when the authorization service is not co-located with the data, leading to inefficiencies in data retrieval.
Innovation Solution
A data retrieval system comprising a middleware subsystem and a learning subsystem that predicts the number of records to retrieve based on performance indicators, coordinating between clients, databases, and authorization services to optimize data retrieval operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If runtime authorization is implemented for each data record, then security and access control are improved, but data retrieval efficiency and response time deteriorate
Solution Approach 1:
The system performs preliminary actions by predicting the number of authorized records in advance before actual data retrieval. The learning module forecasts authorized record counts based on historical authorization outcomes, allowing the middleware to pre-determine optimal batch sizes and retrieval strategies, thus avoiding inefficient runtime authorization checks for individual records
Solution Approach 2:
The system implements feedback mechanisms where the learning module continuously receives performance indicators from actual authorization operations and updates its prediction model accordingly. This feedback loop enables the system to adapt to changing authorization patterns and improve prediction accuracy over time, balancing security requirements with retrieval efficiency
2Adaptability or versatility
If the authorization service is separated from the database, then system modularity and flexibility are improved, but communication overhead and retrieval latency increase
Solution Approach 1:
The learning module performs preliminary predictions of authorized record counts before actual authorization requests are sent to the separated authorization service. This allows the middleware to prepare optimal retrieval strategies in advance, reducing the impact of communication latency with the distributed authorization service
Solution Approach 2:
The learning module acts as an intermediary between the middleware and the separated authorization service. It provides predictive guidance to the middleware about expected authorization outcomes, enabling more efficient coordination with the distributed authorization service without requiring tight coupling or increasing communication overhead
3Productivity
If large batches of records are retrieved for authorization checking, then fewer round-trips are required, but resource congestion and system load increase
Solution Approach 1:
The system dynamically adjusts batch sizes for authorization checking based on predictions from the learning module. Instead of using fixed batch sizes, the middleware adapts retrieval quantities according to predicted authorized record counts and current system conditions, optimizing the balance between throughput and system load
Solution Approach 2:
The system changes operational parameters (batch sizes, retrieval quantities) based on learning module predictions. By adjusting these parameters dynamically according to predicted authorization outcomes and performance indicators, the system optimizes resource utilization while maintaining high authorization throughput
Data Source
AI summary
Methods and systems are disclosed for data retrieval, from databases to clients, in an environment requiring runtime authorization. In response to a request for T data records, a learning module provides a prediction R of a suitable number of data records to retrieve from a database. Following retrieval of R records or record identifiers, authorization is sought from an authorization service, resulting in A of the records being authorized. The A authorized records are returned to the requesting client, and, if more records are needed, T is decremented and the cycle is repeated. A performance notification is provided to the learning module for training, with respect to providing values of prediction R. The performance notification can be based on a measure of authorization service performance, the number A of authorized records, latency, communication or resource costs, a measure of resource congestion, or other parameters. Variants are disclosed.