Federated Query Conversion Across Heterogeneous Data Abstraction Models
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Solution Overview
Problem
Existing database systems face challenges in efficiently retrieving data across multiple devices with different data abstraction models, leading to difficulties in sharing and integrating data due to varying logical field definitions and categorizations.
Innovation Solution
A method is introduced to convert abstract queries between different data abstraction models using standardized metadata concepts, allowing queries to be executed across multiple devices and combining results seamlessly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored in different data abstraction models across multiple devices, then data can be distributed and accessed locally, but data sharing and integration become difficult due to varying logical field definitions
Solution Approach 1:
The patent applies universality by creating a standardized metadata model that can represent multiple different data abstraction models. The standardized logical field definitions serve as a universal interface that can map to various physical field structures across different devices, allowing a single query mechanism to work across heterogeneous data sources without requiring device-specific integration logic
Solution Approach 2:
The patent introduces an intermediary layer consisting of standardized metadata and logical field definitions that sits between the querying system and the diverse physical data storage systems. This intermediary translates between different data abstraction models, converting queries from one model to another, thereby mediating the interaction between heterogeneous devices and eliminating the need for direct complex integration between them
2Ease of operation
If standardized metadata concepts are used to map logical fields across different data abstraction models, then data sharing becomes easier, but query conversion complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining standardized metadata models and logical field definitions before actual data queries are executed. These standardized schemas are established in advance and contain predefined mappings and relationships, allowing query conversion to proceed systematically rather than requiring ad-hoc analysis of each data source's structure during query execution
Solution Approach 2:
The patent utilizes parameter changes by transforming queries through different representation forms while maintaining semantic equivalence. The query conversion process changes parameters such as field names, data types, and structural relationships from one data abstraction model to another, using standardized metadata as the reference framework to guide these parameter transformations systematically
Data Source
AI summary
Embodiments of the invention are generally related to data processing, and more specifically to retrieving results for a query from one or more devices coupled to a network. A first device may receive an abstract query including logical fields defined by a first data abstraction model and retrieve query results stored in the first device. The query may be sent to one or more other devices via the network. The one or more other devices may be configured to convert the abstract query to local abstract queries including logical fields defined in local data abstraction models. The local queries may be issued against local databases to retrieve additional results for the query.