Semantic Graph Subscription System for Scalable Data Dissemination
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Solution Overview
Problem
Existing information management systems face challenges in providing actionable information due to limited selectivity in subscription systems, scalability issues with increasing data volumes, and the difficulty in modifying filtering rules, especially in complex domains like military operations, where rich domain semantics with temporal and geospatial constraints are required.
Innovation Solution
A method and system for providing on-demand access to relevant portions of a semantic graph distributed among semantic servers, allowing clients to create subscriptions, automatically collect and annotate data, and send alerts based on changes matching client interests, using a semantic data model that represents concepts, relationships, and ontologies to manage and scale data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content-based pub/sub systems use complex queries and event semantics with ontology to represent domain-specific knowledge, then the selectivity of subscriptions is improved, but the system fails to scale to cope with increasing volume, variety and velocity of incoming data
Solution Approach 1:
The system segments the semantic processing workload by distributing the semantic graph across multiple semantic servers. Each server maintains a portion of the semantic graph and can independently process subscriptions related to its segment, enabling parallel processing and improving scalability while maintaining complex query capabilities.
Solution Approach 2:
The patent introduces an intermediary layer that mediates between data sources and subscribers. This intermediary manages the semantic graph and subscription matching, allowing complex semantic queries to be processed efficiently without directly burdening the data sources or subscribers, thus improving both selectivity and scalability.
2Adaptability or versatility
If filtering rules are embedded (hard-coded) in the ontology, then the system can handle rich domain semantics, but it becomes very difficult to modify them by users and requires specialized knowledge engineering
Solution Approach 1:
The system implements dynamic filtering rules that can be modified at runtime without requiring system reconfiguration. Users can add, remove, or modify filtering criteria through the interface, and these changes are immediately applied to the semantic graph processing, enabling flexible adaptation to changing domain requirements.
Solution Approach 2:
The patent enables users to perform their own knowledge engineering tasks through the provided interface. Users can independently create, modify, and manage their own filtering rules and subscriptions without requiring specialized knowledge engineering expertise, making the system self-serviceable for domain-specific adaptations.
3Quantity of substance
If the system collects and processes large volumes of data from multiple sources to provide actionable information, then the quantity of information available is improved, but the time required to process and disseminate the information increases
Solution Approach 1:
The system performs preliminary semantic annotation and graph construction in advance, organizing data into a structured semantic graph before queries are submitted. This pre-processing enables rapid subscription matching and information retrieval when events occur, reducing the time required to process and disseminate actionable information.
Solution Approach 2:
The patent implements continuous monitoring and processing of data streams from multiple sources. The system continuously updates the semantic graph and maintains active subscriptions, ensuring that actionable information is identified and disseminated immediately when conditions are met, minimizing delays in information delivery.
Data Source
AI summary
Methods, systems and media are provided for turning large volumes of globally distributed data into actionable information by building a distributed semantic graph and maintaining such graph with up to date changes in data and client needs are provided. The semantic graph can be used to run subscriptions over interconnected semantic servers where each server can be capable of coupling to data sources, client applications and other semantic servers.