Graph Data Structure Incremental Update Query
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Solution Overview
Problem
Existing data retrieval systems for graph data structures face inefficiencies due to the need for extensive batch processing to update lookup tables, leading to increased latency and decreased responsiveness when servicing queries.
Innovation Solution
Implementing a system that tracks incremental updates to graph data structures using key-value indices and change lists, allowing for targeted searches and reducing the need for full graph processing by identifying and updating only impacted nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If batch processing is used to update lookup tables, then data accuracy is maintained, but processing time and latency increase
Solution Approach 1:
The patent segments the graph data structure into multiple partitions or chunks, allowing incremental updates to be applied to specific segments rather than requiring full batch processing of the entire graph. This enables the system to maintain data accuracy while reducing processing latency by updating only the affected portions.
Solution Approach 2:
The system performs preliminary actions by maintaining change lists that track modifications to the graph data structure before queries are executed. This allows the system to pre-process and identify affected nodes and edges, so that when queries arrive, the lookup tables are already updated or can be quickly updated only where necessary.
2Reliability
If full graph processing is performed, then complete data consistency is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent extracts and tracks changes to specific nodes and edges in the graph data structure using change lists. Instead of processing the entire graph, the system extracts only the modified portions and applies updates selectively. This maintains data consistency while dramatically reducing computational resource consumption by avoiding unnecessary processing of unchanged data.
Solution Approach 2:
The system applies partial action by performing updates only on the subset of the graph that has changed, rather than applying full batch processing to the entire graph. The change lists enable the system to identify and process only the necessary portions, achieving data consistency with minimal computational overhead.
3Measurement precision
If lookup tables are updated frequently, then query accuracy is improved, but system responsiveness decreases
Solution Approach 1:
The patent implements a dynamic update mechanism where lookup tables are updated incrementally based on change lists rather than through frequent full batch updates. The system dynamically adjusts the update process by identifying and processing only the changed portions of the graph, maintaining query accuracy while preserving system responsiveness through optimized update timing and scope.
Data Source
AI summary
Certain embodiments involve tracking incremental updates to graph data structures and thereby facilitating efficient data retrieval. For instance, a computing system services a first query for one or more segments of computing devices, online entities, or both. The computing system services the first query by searching of a set of nodes from a graph data structure. The computing system receives a second query after the graph data structure has been modified. The computing system identifies, from a change list for tracking changes to the graph data structure, a subset of the nodes impacted by the modification to the graph data structure. The computing system services the second query by searching the subset of impacted nodes in the graph data structure.