Ontology-Based Data Placement for Distributed Knowledge Bases
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Solution Overview
Problem
Existing systems for managing distributed knowledge bases (KBs) are inefficient due to a lack of understanding of domain ontologies, leading to poor query management and high computational resource usage, especially when data is distributed across sites with varying capabilities and costs.
Innovation Solution
A data orchestrator models query workload information as a hypergraph, generating mappings between concepts and data nodes based on predefined capabilities to efficiently place and store data, reducing computational expense and improving system responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized mediator is used to aggregate data from distributed sources, then data aggregation is achieved, but system efficiency deteriorates and scalability worsens
Solution Approach 1:
The patent divides the centralized mediator architecture into multiple distributed data sites, each capable of independent query processing. Instead of routing all queries through a single centralized mediator, the system segments the mediation function across multiple nodes, allowing parallel processing and eliminating the bottleneck that caused efficiency degradation.
Solution Approach 2:
The patent introduces a new dimension of organization by implementing an ontology-based hierarchical structure that overlays the distributed data sites. This creates a multi-dimensional architecture where data can be accessed both locally at individual sites and globally through the ontology hierarchy, eliminating the need for a single centralized mediation point.
2Device complexity
If existing systems route queries without understanding domain ontology, then system simplicity is maintained, but query routing efficiency deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-building and storing the domain ontology model that captures relationships between data concepts. This ontology is constructed beforehand and used to pre-determine optimal query routing paths, so that when queries arrive, the system can immediately route them efficiently without needing to analyze ontology relationships in real-time.
Solution Approach 2:
The patent introduces an ontology-based intermediary layer that sits between the query interface and the distributed data sites. This intermediary translates high-level queries into optimized routing decisions by understanding domain concepts and relationships, improving query response time without significantly increasing overall system complexity.
3Adaptability or versatility
If data is distributed across sites with varying capabilities, then system versatility is improved, but query routing efficiency deteriorates
Solution Approach 1:
The patent applies local quality by allowing each distributed data site to maintain its own capabilities and characteristics while being integrated into the overall system through the ontology model. The system recognizes and exploits local qualities of different sites (such as specialized data stores, processing capabilities, or geographic locations) to route queries to the most appropriate local resources.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting query routing decisions based on the capabilities and current state of different data sites. The ontology model stores parameters describing each site's capabilities, and the system changes routing parameters in real-time to optimize query execution across the distributed environment.
Data Source
AI summary
Techniques for distributed data placement are provided. Query workload information corresponding to a domain is determined by a data orchestrator, and the query workload information is modeled as a hypergraph, where the hypergraph includes a set of vertices and a set of hyperedges, where each vertex in the set of vertices corresponds to a concept in an ontology associated with the domain. Mappings are generated between concepts and a plurality of data nodes based on the hypergraph and based further on predefined capability of each of the plurality of data nodes. A distributed knowledge base is established based on the generated mappings.