Relation Network Query Index Blocks for Efficient Edge Retrieval
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Solution Overview
Problem
Existing data query methods for relation networks are inefficient, especially for large datasets, and fail to protect privacy data, as they often require traversing all edges to find edges satisfying a filter condition, leading to high time and complexity.
Innovation Solution
A data query method that uses index blocks associated with nodes to quickly identify edges satisfying a filter condition by determining location information from these index blocks, allowing direct retrieval of edges from storage without traversing all edges, thereby improving query efficiency and protecting privacy data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If common graph query method is used to traverse all edges, then query completeness is ensured, but query efficiency deteriorates for large datasets
Solution Approach 1:
The patent segments the graph data into multiple partitions distributed across different storage nodes. Each partition contains a subset of vertices and edges, allowing parallel query processing across multiple nodes simultaneously. This segmentation enables the system to handle large datasets by dividing the traversal workload into manageable chunks that can be processed concurrently, thereby improving query efficiency and reducing traversal time.
Solution Approach 2:
The patent pre-computes and stores indexing structures (such as inverted indexes and adjacency lists) that map vertex identifiers to their connected edges and neighboring vertices. These preliminary indexes are built beforehand and stored in an optimized format, enabling the query system to quickly locate relevant edges without performing exhaustive traversals. This preliminary action significantly reduces the time required for actual query execution on large datasets.
2Measurement precision
If all edge data is stored in detail, then query accuracy is improved, but storage complexity increases
Solution Approach 1:
The patent extracts essential indexing information (vertex identifiers, edge identifiers, and connectivity relationships) from the complete graph data and stores them in separate index structures. The actual detailed edge data is stored in a simplified format, while the complex querying operations are enabled through these extracted indexes. This separation allows the system to maintain query accuracy by preserving the necessary structural information while reducing overall storage complexity through more efficient data organization.
Solution Approach 2:
The patent introduces multiple dimensions to the storage structure by creating hierarchical indexes and multi-level partitions. Instead of storing all edge data in a single flat structure, the system organizes data across multiple dimensions: vertex-level indexes, edge-level indexes, and partition-level metadata. This dimensional organization enables efficient queries by allowing the system to navigate through different levels of abstraction, maintaining accuracy while managing complexity through structured hierarchy.
Data Source
AI summary
Relation network data query includes receiving a query request for an edge using a first node as a starting node, where the edge satisfies a specified filter condition comprising that the edge has a first edge data item and a corresponding first data value. Multiple index blocks associated with the first node are obtained from a non-volatile storage storing a relation network. A first index block corresponding to the first edge data item is determined from the multiple index blocks based on a correspondence between an index block and an edge data item, where the first index block comprises multiple data values of the first edge data item and location information of edges having the multiple data values and using the first node as starting nodes. Location information of an edge having the corresponding first data value is determined from the first index block and the edge obtained.