Unified Query Engine Using First-Order Logic for Graph-Relational Data
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Solution Overview
Problem
Existing systems lack an efficient method for querying databases that combine relational and graph data, as they often require data conversion, emulation, and struggle with semantic compatibility and schema dependencies, leading to inefficiencies and limitations in handling unstructured data.
Innovation Solution
A computer-implemented method that generates an intermediate representation compatible with first-order logic to execute queries on both relational and graph data without conversion, using a unified query engine that translates SPARQL and SQL queries into predicate calculus, allowing direct execution on native data formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data conversion and emulation are used to query combined relational and graph data, then query compatibility is improved, but processing efficiency and time are worsened
Solution Approach 1:
The patent introduces an intermediary translation layer that converts graph database query languages (such as Cypher or Gremlin) into SQL queries. This mediator enables unified querying of both relational and graph data through a single SQL interface without requiring data conversion or emulation, thus maintaining high processing efficiency while achieving query compatibility.
Solution Approach 2:
The patent creates a universal query interface that handles both relational and graph data types through a single SQL-based system. The query engine is designed to automatically detect data types and route queries appropriately, eliminating the need for separate query systems and improving overall processing efficiency while maintaining versatility.
2Reliability
If data conversion is performed to handle semantic compatibility, then query execution is improved, but data loss and time consumption are worsened
Solution Approach 1:
The patent implements preliminary schema validation and type detection mechanisms that analyze the target database schema before query execution. This preliminary action identifies the appropriate data type (relational or graph) and prepares the necessary translation rules in advance, eliminating the need for runtime data conversion and reducing time consumption while ensuring semantic compatibility.
Solution Approach 2:
The patent dynamically changes query parameters based on the detected data type. When graph data is detected, the system automatically adjusts query parameters and translation rules to match graph database semantics without requiring actual data conversion, thus preventing data loss and reducing processing time.
3Stability of the object's composition
If separate query systems are used for relational and graph data, then data format integrity is improved, but system complexity is worsened
Solution Approach 1:
The patent merges the functionality of separate relational and graph query systems into a single unified SQL-based engine. The system automatically detects the data type and applies appropriate query translation rules, maintaining data format integrity for both relational and graph data while significantly reducing system complexity by eliminating the need for multiple separate query systems.
Data Source
AI summary
A computer-implemented method for querying by a query engine on a database including relational data and graph data, the method including receiving a query by the query engine, the query being in a language compatible with a first order logic paradigm. The method further includes generating an intermediate representation, the IR being compatible with a first order logic paradigm. The method further includes using the intermediate representation of the query for executing the query on the relational data and the graph data of the database.