Hybrid Data Analytics Algorithm Segmentation for IoT Edge Cloud
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Solution Overview
Problem
Current data analytics systems in cloud computing face challenges in optimizing the distribution of analytical tasks between local devices and cloud platforms, balancing real-time processing needs with network bandwidth and security considerations, particularly in industrial IoT applications.
Innovation Solution
The system modularizes analytical algorithms into sub-algorithms that can be executed independently or in parallel, with specific affinity indicators determining their optimal execution location between local devices and cloud platforms, allowing for dynamic reassignment based on resource availability and performance requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data analytics is performed on the cloud computing platform, then the quality and speed of data analytics is improved, but network bandwidth consumption increases and real-time processing capability deteriorates
Solution Approach 1:
The patent segments the analytical algorithm into multiple sub-algorithms that can be distributed and executed across different locations (local device and cloud platform). This segmentation allows critical real-time processing to occur locally while less time-sensitive analytics are performed in the cloud, thereby improving data analytics quality without proportionally increasing network bandwidth consumption.
2Speed
If data analytics is performed at the local device, then real-time processing capability is improved, but data analytics quality and computational power are limited
Solution Approach 1:
The analytical algorithm is divided into sub-algorithms that can be selectively executed at different locations. Time-critical sub-algorithms are executed locally on the device to maintain real-time processing capability, while computationally intensive sub-algorithms are executed on the cloud platform to enhance overall data analytics quality.
Solution Approach 2:
The patent introduces a spatial dimension to data analytics by distributing computation across multiple locations (device and cloud). This multi-dimensional execution environment allows the system to simultaneously achieve real-time processing for local sub-algorithms and high-quality analytics for cloud-based sub-algorithms.
3Reliability
If analytical algorithms are executed centrally in the cloud computing platform, then intellectual property protection is improved, but device complexity and deployment difficulty increase
Solution Approach 1:
The patent segments the analytical algorithm into sub-algorithms with different affinity indicators. Sub-algorithms with high cloud affinity are executed centrally in the cloud platform, protecting sensitive intellectual property. Sub-algorithms with low cloud affinity are executed locally, reducing deployment complexity and avoiding the need to deploy entire complex algorithms to each device.
4Productivity
If the system uses a hybrid approach with sub-algorithms executed at both device and cloud, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The patent implements a dynamic execution environment where the system can adaptively assign sub-algorithms to device or cloud based on real-time conditions such as resource availability, data characteristics, and performance requirements. This dynamic approach optimizes resource utilization while managing system complexity through automated decision-making rather than static complex configurations.
Data Source
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AI summary
The present invention relates to a system for data analytics in a network between one or more local device(s) (130) and a cloud computing platform (120), in which data collected and/or stored on the local device(s) (130) and/or stored on the cloud computing platform (120) are processed by an analytical algorithm (A) which is subdivided into at least two sub-algorithms (SA1, SA2), wherein one sub-algorithm (SA1) is executed on the local device(s) (130) and the other sub-algorithm (SA2) is executed on the cloud computing platform (120).