Sampled Database Access Control Through Query Domains
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Solution Overview
Problem
Data latency and inconsistency arise due to data sampling in distributed systems, leading to unexpected query results and challenges in managing access to sampled databases.
Innovation Solution
Implement a computer program product with domain-based access control mechanisms to manage access to sampled databases, using domain definitions and sampling models to determine query eligibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data sampling is applied to minimize replication costs, then data replication cost is reduced, but data consistency and query accuracy deteriorate
Solution Approach 1:
The system performs preliminary actions by defining domain specifications and sampling models in advance. Query domain definitions are established before queries are executed, and sampling models are pre-configured to determine which data changes should be replicated. This allows the system to proactively manage data consistency rather than reactively fixing issues after they occur.
Solution Approach 2:
The system changes parameters by introducing domain definitions with specific parameters (domain ID, domain name, sampling model identifiers) and by dynamically adjusting which data changes are replicated based on sampling model criteria. This allows flexible control over replication behavior while maintaining consistency within defined domains.
2Productivity
If data sampling is applied to reduce replication overhead, then processing efficiency is improved, but query result accuracy deteriorates
Solution Approach 1:
The system applies local quality by allowing different domains to have different sampling models and consistency requirements. Each domain can be configured with its own sampling strategy appropriate to its specific needs, rather than applying a uniform sampling approach across all data. This ensures high accuracy for critical domains while maintaining efficiency for less critical ones.
Solution Approach 2:
The system segments data into distinct domains with unique identifiers and characteristics. By dividing the data space into manageable domains, the system can apply targeted sampling strategies to each segment, improving overall query accuracy while maintaining processing efficiency through selective replication.
3Reliability
If domain-based access control is implemented to manage sampled data, then data access consistency is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional domain definition structure that serves multiple purposes: it defines data domains, specifies sampling models, controls access permissions, and manages query routing. This single domain definition mechanism handles what would otherwise require multiple separate systems, reducing overall complexity while maintaining consistency.
Data Source
AI summary
A request to query sampled data of a database is obtained. Based on the request, a determination is made as to whether access to the sampled data of the database is to be allowed. To determine whether access to the sampled data of the database is to be allowed, a query domain defined for the query is checked with a data domain defined for the sampled data. Based on determining that access is to be allowed, access to the sampled data, via the query, is allowed.