Incremental Query Output Generation for Distributed Data Retrieval
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Solution Overview
Problem
Existing graph-query-based data retrieval systems in distributed database environments face inefficiencies due to high latency when retrieving a portion of requested data, especially when one data source experiences performance degradation, leading to slow query outputs despite other data being of greater importance to the user.
Innovation Solution
The system generates partial query outputs by incrementally updating the query processing model based on received data from various sources, determining structural similarity measures to decide when to transmit data, allowing for efficient incremental data transmission and improved latency management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system waits for all data sources to complete data retrieval before generating query output, then data completeness is improved, but query latency increases significantly
Solution Approach 1:
The system generates partial query outputs when sufficient data is available from some data sources, rather than waiting for all sources. This allows the system to deliver useful results incrementally, improving response time while maintaining acceptable data completeness through selective output generation based on completeness measures.
Solution Approach 2:
The system performs preliminary assessment of data completeness and significance before generating full query outputs. By evaluating completeness measures and data significance in advance, the system can determine when partial outputs are sufficient, avoiding unnecessary waiting for slower data sources while still providing meaningful results.
2Reliability
If the system retrieves data from all data sources simultaneously, then data completeness is improved, but system resource consumption increases
Solution Approach 1:
The system dynamically adjusts data retrieval strategies based on real-time assessments of data completeness measures and significance. Rather than uniformly retrieving from all sources, the system adapts its retrieval behavior to balance completeness requirements with resource consumption, optimizing performance based on current system state and data priorities.
Solution Approach 2:
The system applies different retrieval strategies to different data sources based on their individual characteristics, significance, and contribution to query completeness. High-priority data sources receive focused retrieval efforts while lower-priority sources may be deferred or sampled, optimizing resource allocation across the distributed system.
3Manufacturing precision
If the system processes complete query results before transmission, then output accuracy is improved, but transmission latency increases
Solution Approach 1:
The system transmits partial query outputs when completeness measures indicate sufficient data quality, rather than waiting for complete processing of all results. This incremental transmission approach reduces latency while maintaining acceptable accuracy through selective output generation based on real-time completeness assessment.
Solution Approach 2:
The system periodically evaluates data completeness measures during the query processing pipeline and transmits outputs when thresholds are met. This periodic assessment and transmission strategy balances accuracy requirements with latency reduction, allowing multiple incremental transmissions rather than a single delayed complete output.
Data Source
AI summary
Systems and methods provide techniques for incremental data retrieval using structural metadata. In one embodiments, a method includes identifying a query definition model associated with the data retrieval query, transmitting a respective data retrieval request to each data source to obtain a per-source portion of a plurality of requested data nodes that is associated with the data source; and upon obtaining each particular per-source portion, updating a query processing model for the data retrieval query based on the particular per-source portion, determining a model completeness measure for the updated query processing model, determining whether the model completeness measure satisfies one or more completeness threshold criteria, and in response to determining that the model completeness measure satisfies the one or more completeness threshold criteria, generating a partial query output for the data retrieval query based on the updated query processing model.