Ontology Harmonization Mediator for Heterogeneous Database Querying
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Solution Overview
Problem
Traditional ETL approaches face challenges in data integration across large-scale, heterogeneous databases due to differences in data formats, granularities, schemas, and distributions, leading to impractical data conversion and synchronization issues, especially at cloud scale.
Innovation Solution
A multi-database query system employing ontology harmonization and mediation (OHM) that translates queries across different ontologies and data sources, allowing for simultaneous querying of heterogeneous databases like triple stores, relational databases, and cloud databases without transforming data into a common format, using open semantic standards like OWL and RDF for data mediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ETL approaches are used to integrate data from heterogeneous databases, then data can be converted to a common model, but data integration time and computational resources increase significantly at petabyte-scale
Solution Approach 1:
The patent introduces an ontology harmonization and mediation (OHM) engine as an intermediary layer between heterogeneous databases and query interfaces. This mediator translates queries across different ontologies and data models without requiring physical data movement or conversion, enabling efficient integration of petabyte-scale data from diverse sources including triple stores, relational databases, and cloud databases while avoiding the time-consuming ETL process
Solution Approach 2:
Instead of physically copying and transforming data through ETL processes, the system creates virtual copies of data through ontology mappings and federated query mechanisms. The OHM engine maintains logical representations of data from multiple sources without duplicating the actual petabyte-scale data, allowing simultaneous querying of heterogeneous databases while preserving native data distributions
2Adaptability or versatility
If data is duplicated and converted to a common model in traditional ETL, then data can be queried across sources, but synchronization issues arise at larger scale and BASE semantics
Solution Approach 1:
The OHM engine serves as a mediator that handles ontology harmonization between disparate data sources. It translates queries and results between different ontologies in real-time, enabling data model compatibility without physical data duplication. This approach maintains synchronization consistency by working with data in its native location rather than maintaining copies across multiple systems
Solution Approach 2:
The system employs dynamic ontology mapping and query translation capabilities that adapt to different data sources and their changing schemas. The OHM engine can dynamically adjust translation strategies based on the specific ontologies involved, allowing the system to handle evolving data models while maintaining compatibility across heterogeneous sources
3Adaptability or versatility
If traditional ETL approaches are used, then data can be integrated across sources, but impedance mismatch between data models is baked into the transformed data
Solution Approach 1:
The OHM engine acts as an intermediary that manages the complexity of ontology harmonization centrally rather than requiring complex transformation logic embedded in each data source or query interface. It provides a unified mechanism for handling impedance mismatches between different data models, reducing overall system complexity while enabling cross-source data access
4Quantity of substance
If the scale of data increases to cloud scale, then more data can be stored, but traditional ETL approaches become impractical for conversion and redundant storage
Solution Approach 1:
The system uses virtual copying through ontology mappings rather than physical data duplication. The OHM engine creates logical representations of petabyte-scale data from multiple sources without requiring redundant storage of the actual data, making data conversion and integration feasible at cloud scale
Solution Approach 2:
The OHM engine provides an intermediary layer that enables querying of cloud-scale heterogeneous databases without requiring impractical data conversion processes. It translates queries across different ontologies and data models on-demand, making large-scale data integration practical while preserving the native format and location of the data
Data Source
AI summary
A method and system for harmonizing and mediating ontologies to search across large data sources is disclosed. The method comprises receiving a query targeting a first ontology. The method further comprises translating the query into one or more translated queries, each translated query targeting a respective ontology different from the first ontology. For each of the queries, issuing the query to a respective database organized according to the respective ontology of the query, and receiving a respective result set for the query, wherein the respective result set corresponds to the respective ontology of the query. The method further comprises translating the respective result set into a translated result set corresponding to the first ontology, aggregating the result sets into an aggregated result set corresponding to the first ontology, and returning the aggregated results set corresponding to the first ontology.