Distributed Graph Query Parallelization Across Heterogeneous Data Sources

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Solution Overview

Problem

Conventional technologies lack the capability to automatically programmatically parallelize distributed graph queries across heterogeneous data sources, leading to increased latency, computational expense, and inefficient resource utilization in searching operations.

Innovation Solution

A graph metaphor representing distributed data sources is used to model entities and relationships, enabling programmatically parallelized query plans that execute queries across multiple data sources simultaneously, optimizing data access and reducing latency through parallelization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If distributed graph queries are executed sequentially across multiple data sources, then system complexity is reduced and ease of operation is maintained, but query execution time increases and productivity decreases

Engineering Contradiction:

Improvequery execution speedVSAvoidquery parallelization complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the distributed graph query into multiple independent sub-queries that can be executed in parallel across different data sources. The query plan is divided into separate tasks, each handling a specific data source or query component, allowing simultaneous execution without interfering with each other, thus improving productivity while managing complexity through structured decomposition

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by pre-processing the graph query to generate a query plan that identifies parallelizable operations before execution. The query is analyzed and transformed into a format that exposes parallel execution opportunities, with tasks scheduled and prepared in advance, enabling efficient parallel execution without requiring complex real-time coordination during query processing

Inventive Principle:

Principle #10Preliminary action

2Loss of time

If parallelized queries are executed across distributed data sources, then query execution time is reduced and productivity improves, but computational expense and resource consumption increase

Engineering Contradiction:

Improvequery latencyVSAvoidcomputational resource consumption

Core Design Contradiction:

Loss of timeVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by executing only the necessary parallel queries required to satisfy the graph query requirements. The system identifies and executes a subset of parallel tasks that provide the needed results without unnecessarily querying all available data sources, reducing computational expense while maintaining the latency benefits of parallelization

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system ensures continuity of useful action by maintaining an active query execution pipeline where multiple queries are processed continuously in parallel across data sources. Rather than sequential processing with idle periods, the system keeps computational resources continuously engaged in useful query operations, improving time efficiency while optimizing resource utilization through sustained parallel activity

Inventive Principle:

Principle #20Continuity of useful action

3Adaptability or versatility

If data is accessed through the original database format rather than a graph database, then adaptability to existing systems is maintained and ease of operation is improved, but the ability to perform graph-based operations efficiently is reduced

Engineering Contradiction:

Improvecompatibility with existing databasesVSAvoidgraph query processing efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent introduces an intermediary layer that translates graph-based queries into operations compatible with existing database formats. This intermediary translates the graph query semantics into the native query language and data access patterns of the underlying database system, maintaining adaptability to existing databases while enabling efficient graph-based operations through the translation layer that optimizes query execution for the target system

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12511283B2Parallelization of distributed graph queries

Publication Date: 2025.12.30 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Technology is disclosed for programmatically parallelizing distributed graph queries of a graph metaphor of distributed data sources through various applications or platforms. Query candidates corresponding to distributed data sources are determined by applying a graph query to a graph metaphor of the distributed data sources. A set of query steps representing a set of distributed queries of the query candidates are determined based on corresponding properties of the query candidates from the graph metaphor. The set of query steps are executed in parallel in order to determine a response to the graph query. The data is provided in response to the graph query.