Neutral Ontology Model for Distributed Database Querying
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Solution Overview
Problem
Current technologies face challenges in providing consistent access to and integrating maintenance and supply chain data across heterogeneous databases in distributed environments, due to semantic heterogeneity and proprietary data sources, leading to difficulties in data access and integration.
Innovation Solution
A neutral ontology model is constructed to serve as a common semantic interface for querying distributed databases, using metadata mappings to abstract and mediate queries across different databases, enabling users to access data without knowledge of specific data systems or query languages, and utilizing a Web Service architecture for scalable integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If direct access to heterogeneous databases is used, then data access speed is improved, but system complexity and difficulty of integration increase
Solution Approach 1:
The patent introduces an intermediary layer (the system with neutral ontology model and mapping mechanisms) between users and heterogeneous databases. This intermediary translates user queries written in neutral terminology into database-specific queries, and translates database results back into neutral format. This resolves the contradiction by maintaining fast direct database access while shielding users from integration complexity through automatic query translation and semantic mapping.
Solution Approach 2:
The patent segments the data access system into distinct functional layers: the neutral ontology model layer, the mapping layer, and the database-specific implementation layer. This segmentation allows each layer to operate independently - the neutral layer handles user interactions without knowing database complexities, while database layers maintain their own optimized access paths. This resolves the contradiction by separating the simplicity requirement from the speed requirement into different layers.
2Quantity of substance
If proprietary data sources are integrated, then data completeness is improved, but semantic heterogeneity and access difficulty increase
Solution Approach 1:
The patent creates a homogeneous interface layer (the neutral ontology model) that presents a unified, consistent data access interface to users, regardless of the heterogeneity of underlying proprietary databases. All databases are accessed through the same neutral terminology and query mechanisms, making the system easy to operate while maintaining access to diverse data sources. This resolves the contradiction by providing data completeness through multiple sources while hiding semantic heterogeneity behind a homogeneous interface.
Solution Approach 2:
The neutral ontology model acts as an intermediary that mediates between users and proprietary data sources with different semantics. It provides semantic mapping and translation capabilities, allowing users to access complete data from multiple proprietary sources without dealing with their individual semantic differences. This resolves the contradiction by maintaining data completeness while improving ease of operation through automatic semantic translation.
3Ease of operation
If a unified query interface is implemented, then ease of use is improved, but adaptability to different database structures decreases
Solution Approach 1:
The patent implements a dynamic mapping system where the relationship between the neutral ontology model and specific database structures can be flexibly configured and adapted. The mapping mechanisms are not rigid but can be dynamically adjusted to accommodate different database structures, schemas, and proprietary formats. This resolves the contradiction by providing a unified ease-to-use interface while maintaining high adaptability through dynamic, configurable mapping relationships.
Solution Approach 2:
The neutral ontology model serves as a universal interface that can work with multiple different database structures and proprietary data sources. It provides multi-functional capabilities to translate between various database schemas and a common query language, making the system both easy to use and highly adaptable. This resolves the contradiction by creating a universal interface layer that maintains ease of use while adapting to diverse underlying database structures.
Data Source
AI summary
According to an embodiment, a method includes constructing a neutral ontology model of a query front end characterized by ontology schemata which subsume the plurality of different databases on the network in order to provide a common semantic interface for use in generating queries for data from any of the different databases, importing respective database metadata representing logical and physical structures of each database subscribed for receiving queries for data from the database using the query front end, constructing mappings of the database metadata representing the logical and physical structures of each subscribed database to the ontology schemata of the query front end, and storing the constructed mappings for use by the query front end for queries through the common semantic interface of the neutral ontology model for data from any of the different databases.