Ontology-Driven Contextual Mediation for Semantic Conflict Resolution
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Solution Overview
Problem
Current contextual mediation technologies face challenges in accurately identifying and processing relevant data within a computing environment, particularly in resolving semantic inter-operation issues and providing context-sensitive information to end users.
Innovation Solution
An ontology-driven contextual mediation method and system that utilizes a COIN mediator to collect and correlate events with operational meta-data and pre-defined symptoms, producing context-sensitive events that can be associated with corresponding symptoms, thereby resolving semantic conflicts and providing meaningful context to end users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mediation architectures are used to resolve semantic inter-operation issues, then uniform interfaces can be provided to end users, but the system cannot accurately identify and process context-sensitive relevant data
Solution Approach 1:
The patent introduces a contextual mediator as an intermediary component that sits between event sources and consumers. This mediator enriches events with contextual information from multiple sources (operational metadata, solution topology, workload patterns) before forwarding them to consumers, thereby enabling accurate identification of relevant data without requiring complex changes to the underlying architecture.
Solution Approach 2:
The mediation architecture is segmented into distinct functional components: event collectors that gather raw events, a contextual mediator that enriches events with context, and consumers that process the enriched events. This segmentation allows each component to focus on specific tasks, improving overall system efficiency and accuracy while managing complexity through modular design.
2Reliability
If events are collected and processed without contextual mediation, then system performance is maintained, but semantic conflicts cannot be resolved and context-sensitive information cannot be provided
Solution Approach 1:
The system performs preliminary actions by collecting and storing contextual information (operational metadata, solution topology, workload patterns) before events need to be processed. This pre-computed contextual data is readily available when events arrive, enabling the mediator to quickly resolve semantic conflicts and provide context-sensitive information without adding significant processing complexity to the event path.
3Loss of information
If comprehensive operational metadata is collected to provide context, then context-sensitive events can be produced, but data processing overhead increases
Solution Approach 1:
The contextual mediator applies local quality by selectively enriching events with only the specific contextual information relevant to each event type and consumer needs. Rather than attaching all available metadata to every event, the mediator intelligently selects and attaches only the necessary context, thereby maintaining information quality while minimizing processing overhead.
Data Source
AI summary
A method for ontologically driving context mediation in a computing system can include collecting events arising from a solution in a computing environment, loading operational meta-data for the solution, contextually mediating, for example context interchange (COIN) mediating, the collected events with the operational meta-data to produce context sensitive events, and correlating the context sensitive events with corresponding symptoms in a display to an end user in the computing environment.