Statistics-Aware Sub-Graph Query Engine Indexing
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Solution Overview
Problem
As graph data structures grow in size and complexity, sub-graph querying becomes increasingly time-consuming and resource-intensive, necessitating improved methods for efficient data retrieval.
Innovation Solution
The generation of multiple graph indices with different attribute prioritization formats allows for selective index usage based on edge-based attributes, optimizing data retrieval by dividing queries into portions processed by the most suitable index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a single graph index is used for sub-graph querying, then the query processing is simple, but the query latency increases as the graph grows in size and complexity
Solution Approach 1:
The patent divides the graph index into multiple specialized indices, each optimized for specific edge-based attributes (e.g., source vertex, destination vertex, edge weight). This segmentation allows the system to select the most appropriate index for each query type, reducing query latency while maintaining manageable complexity through organized specialization.
Solution Approach 2:
The patent implements dynamic index selection based on query characteristics and graph statistics. The system adapts by choosing different indices depending on the specific query parameters and the current state of the graph, allowing optimal performance across varying graph sizes and complexities without requiring a single static index structure.
2Productivity
If multiple graph indices are generated with different attribute prioritization formats, then query latency is reduced by selecting the optimal index, but the device complexity increases
Solution Approach 1:
Each graph index is optimized with specific attribute prioritization formats tailored to particular query patterns (e.g., one index prioritizes source vertex for outgoing edge queries, another prioritizes destination vertex for incoming edge queries). This local optimization ensures that each index excels at its specific function, improving overall retrieval speed while keeping each individual index relatively simple.
Solution Approach 2:
The patent changes the organizational parameters of the graph indices based on different attribute priorities. By creating indices with varying attribute ordering and prioritization schemes, the system can adapt to different query requirements, improving productivity while the parameter variations provide a systematic way to manage the complexity of multiple indices.
3Reliability
If queries are divided into portions for processing by different indices, then retrieval performance is enhanced for varying edge distributions, but the query processing complexity increases
Solution Approach 1:
The patent performs preliminary analysis of query characteristics and graph statistics before executing the actual query. By pre-determining which index portions to use and how to split the query based on edge distribution patterns, the system reduces retrieval performance variability while the preliminary processing step organizes the complexity into a manageable pre-planning phase.
Data Source
AI summary
Methods and systems are presented for retrieving data associated with one or more portions of a graph by a computer system. Multiple graph indices are generated based on the graph. Each of the multiple graph indices stores records of data representing the graph in different formats. Upon receiving a request for accessing a sub-graph of the graph, edge-based attributes of the sub-graph are analyzed. The sub-graph may be divided into a first portion and a second portion of the sub-graph. A first graph index may be selected for retrieving records associated with the first portion of the sub-graph based on the edge-based attributes of the first portion of the sub-graph. A second graph index may be selected for retrieving records associated with the second portion of the sub-graph based on the edge-based attributes of the second portion of the sub-graph.