Dataset Semantic Broker for M2M Interoperability
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Solution Overview
Problem
Conventional Machine-to-Machine (M2M) systems lack dynamic and semantic data management, leading to static and non-reusable datasets, poor interoperability, and inadequate real-time data processing and virtualization, which limits their ability to provide effective and adaptive services.
Innovation Solution
A Dataset Semantic Broker (DSB) platform that manages semantic representations of datasets, enabling automatic association, virtualization, and application upgrades, allowing devices to interact based on data meaning and concepts, and facilitating dynamic dataset creation and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If datasets are stored in conventional databases without semantic representation, then data storage is simple and direct, but interoperability between devices deteriorates and data meaning cannot be understood
Solution Approach 1:
The patent introduces an intermediary layer (semantic broker, ontology repository, dataset link manager) between the database and devices. This intermediary translates and manages semantic representations of datasets, enabling devices to understand data meaning without directly accessing complex database structures. The intermediary resolves the contradiction by providing semantic enrichment while abstracting complexity from the data storage layer.
Solution Approach 2:
The patent segments the data management system into distinct functional components: ontology repository, dataset link manager, semantic broker, and virtual dataset manager. Each component handles specific aspects of semantic data management independently. This segmentation improves interoperability through specialized semantic processing while distributing complexity across multiple manageable modules rather than concentrating it in a single complex system.
2Adaptability or versatility
If M2M systems use static datasets stored in databases, then data storage is stable, but the systems become non-reusable and cannot produce new datasets dynamically
Solution Approach 1:
The patent implements dynamics by introducing the virtual dataset manager and semantic broker that enable datasets to be created, modified, and accessed dynamically at runtime. The system transitions from static database storage to dynamic virtual datasets that can be generated on-demand based on device needs and semantic queries, allowing M2M systems to adapt and produce new datasets without permanent storage changes.
Solution Approach 2:
The patent adds a semantic dimension to traditional data storage by layering ontology-based semantic representations over conventional databases. This additional dimension enables dynamic dataset creation through semantic composition while maintaining the underlying stable database structure, resolving the contradiction between stability and adaptability.
3Adaptability or versatility
If applications are bound to specific information without semantic representation, then application development is straightforward, but the logic cannot exploit data meaning dynamically
Solution Approach 1:
The patent makes application logic universal by enabling it to query and process any dataset through semantic representations rather than being bound to specific data formats. The semantic broker and ontology repository provide a universal interface that allows applications to exploit data meaning dynamically across different data types and sources, increasing reusability while the semantic abstraction layer manages the complexity.
4Productivity
If conventional M2M systems lack semantic data management, then system architecture is simple, but real-time data processing and virtualization are inadequate
Solution Approach 1:
The patent applies preliminary action by pre-defining ontologies and semantic representations of datasets before runtime processing. This preliminary semantic structuring enables efficient real-time data processing because the semantic broker can quickly interpret and route data based on pre-established semantic models, rather than processing raw data without contextual understanding. The complexity is managed by preparing semantic frameworks in advance.
Data Source
AI summary
A method and a system for data management in the interaction between machines in a deployed system in which a plurality of devices are made to use a Dataset Semantic Broker (DBS) platform that stores ontologies, wherein the ontologies comprise semantic representation of datasets, the method providing automatic linking of datasets and devices.