Semantic Rule Engine for Embedded Memory Constraints
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Solution Overview
Problem
Embedded devices with constrained system resources face limitations in storing and utilizing semantic information due to memory constraints, which restricts the amount of data and relationships that can be directly stored, hindering applications that require detailed device metadata.
Innovation Solution
A semantic rule engine is implemented within the embedded device, utilizing an extensible set of rules to derive additional semantic information from existing data, applying rules based on name matching, containment, attributes, and relationships, rather than storing tags and relationships directly, allowing for dynamic generation of metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If semantic information is stored directly in embedded devices, then data availability and semantic richness are improved, but memory usage increases beyond constrained capacity
Solution Approach 1:
The patent extracts semantic information generation from the embedded device by implementing a cloud-based semantic information service. The embedded device only stores minimal local semantic information, while the cloud service dynamically generates and provides additional semantic information on demand, effectively removing the burden of storing extensive semantic data from the constrained embedded memory.
Solution Approach 2:
The patent introduces a cloud-based semantic information service as an intermediary between the embedded device and comprehensive semantic information. This mediator generates semantic information dynamically based on device data and provides it to the embedded device when needed, eliminating the need for the embedded device to store all semantic information locally.
2Productivity
If extensive semantic information is stored in embedded devices, then data utilization and analytics capability are improved, but device complexity increases
Solution Approach 1:
The patent extracts complex semantic information processing from the embedded device and relocates it to a cloud-based service. The embedded device maintains only essential local semantic information, while the cloud service handles complex semantic generation, relationship mapping, and analytics support, thereby reducing device complexity while preserving data utilization capabilities.
Solution Approach 2:
The patent replaces the mechanical approach of storing extensive semantic information locally in the embedded device with a network-based approach using cloud computing resources. This substitution allows the embedded device to access comprehensive semantic information through network communication rather than local storage, reducing device complexity while maintaining analytics capability.
3Quantity of substance
If semantic information is derived dynamically using rule engines, then memory usage is reduced, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-defining semantic rules and relationships in the cloud-based semantic information service. These rules are prepared in advance and can be efficiently applied when semantic information is needed, reducing the computational burden and time required for dynamic derivation compared to creating semantic information from scratch at runtime.
Solution Approach 2:
The cloud-based semantic information service acts as an intermediary that handles the computationally intensive task of dynamic semantic information derivation. By moving this processing to the cloud, the embedded device avoids the time and resource overhead of running complex rule engines locally, while still benefiting from dynamic semantic information generation.
Data Source
AI summary
A system and approach for deriving data for a constrained environment of a controller such as, for example, an embedded device. The controller may incorporate a processor and a memory connected to the processor. The memory may have a constrained capacity. The memory may contain an extensible set of rules for deriving additional semantic information from available information at the embedded device. The processor and the memory with the extensible set of rules may constitute a semantic rule engine. The semantic rule engine may apply the extensible set of rules to the available information to derive the additional semantic information.