Auto-ID Gateway Rule Engine for Data Processing
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Solution Overview
Problem
Existing auto-ID systems face challenges in processing vast amounts of data due to limited memory and computation power at lower-level devices, making it difficult to execute complex business rules efficiently, especially when these rules change over time.
Innovation Solution
A distributed and flexible architecture is introduced, featuring an auto-ID processing gateway that processes data using a rule engine with a component or service-oriented architecture, allowing for flexible configuration of rule execution flow and omitting unnecessary processing steps, enabling sophisticated rules to be loaded and executed on devices with limited capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complex business rules are executed at lower-level auto-ID devices, then processing capability is improved, but device memory and computation power requirements increase
Solution Approach 1:
The system divides the data processing architecture into multiple layers: auto-ID devices at the edge layer that generate data, an intermediate processing layer with rule engines that execute business rules, and application layers that consume processed results. This segmentation allows complex rules to be executed at the intermediate layer rather than requiring enhanced capabilities at the resource-constrained edge devices.
Solution Approach 2:
An intermediate processing layer with rule engines acts as a mediator between auto-ID devices and application systems. This intermediary component handles the execution of complex business rules, filtering and processing raw auto-ID data before forwarding results to applications, thereby protecting resource-constrained devices from needing to execute complex logic locally.
2Adaptability or versatility
If rule execution flow is fixed, then system simplicity is maintained, but flexibility to adapt to changing business rules decreases
Solution Approach 1:
The rule engine implements dynamic rule execution flow where the processing path is determined at runtime based on the specific business rules being executed. Different rule sets can activate different processing workflows, allowing the system to adapt to changing business requirements without reconfiguration. The engine can dynamically load, unload, and switch between different rule collections.
Solution Approach 2:
The rule engine is designed as a universal processing component that can execute multiple types of business rules through a common architecture. It supports various rule formats, execution modes, and processing strategies within a single system, enabling one component to serve multiple functions across different business scenarios without requiring separate specialized systems.
3Productivity
If all processing steps are executed for every data item, then processing completeness is ensured, but processing time and resource consumption increase
Solution Approach 1:
The rule engine implements selective rule execution where only the subset of rules relevant to each specific data item or business context is executed. Rather than applying all possible processing steps uniformly to every data item, the system activates only those rules necessary for the current processing scenario, reducing unnecessary computational overhead while maintaining required processing completeness.
Solution Approach 2:
The system dynamically adjusts processing parameters such as rule activation states, processing depth, and execution priorities based on data characteristics and business context. This allows the processing pipeline to adapt its resource consumption profile, executing more comprehensive analysis when needed and more streamlined processing when sufficient, thereby optimizing the balance between processing completeness and throughput.
Data Source
AI summary
Embodiments of the present invention improve data processing, and in particular, improve rule engines for data processing and improve processing auto-ID data. In one embodiment, the present invention includes a gateway layer between a auto-ID device layer and an auto-ID node layer for processing data. The gateway layer may incorporate a rule engine for processing data. In another embodiment, the present invention includes a rule processor where rules are activated and deactivated automatically to reduce system overhead. In another embodiment, the rule engine is composed of multiple services that can be omitted if they are not needed for the processing of a given rule set to reduce the footprint and resource consumption of the rule engine.