Edge Computing Layer for AI Data Pre-processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current edge computing systems face challenges in efficiently processing and analyzing data close to its source, leading to latency, privacy concerns, and increased costs due to reliance on centralized data centers.
Innovation Solution
Implementing an edge computing layer between the data source and traditional data centers to pre-process data and perform initial artificial intelligence and advanced analytics, with further analysis occurring in a cloud environment, utilizing edge devices and servers for real-time processing and minimizing data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is processed and analyzed at a centralized data center, then comprehensive analytics can be performed, but latency increases and real-time decision-making is hindered
Solution Approach 1:
The patent segments the data processing function into two parts: edge computing devices perform initial data collection and pre-processing locally, while the centralized data center performs comprehensive analytics. This segmentation allows time-sensitive operations to occur locally without waiting for centralized processing, thereby reducing latency while maintaining comprehensive analytical capabilities at the data center.
2Loss of information
If data is transmitted to centralized data centers for processing, then comprehensive analytics can be achieved, but costs increase due to data transmission and storage
Solution Approach 1:
The patent extracts the initial data processing function from the centralized data center and places it at edge computing devices near the data sources. By taking out the pre-processing step from the centralized system, the patent reduces the volume of data that needs to be transmitted and stored at the data center, thereby reducing processing costs while maintaining comprehensive analytics capability through subsequent processing at the data center.
3Loss of information
If data is processed at centralized data centers, then comprehensive analytics can be performed, but privacy concerns increase due to centralized data storage
Solution Approach 1:
The patent applies local quality by keeping data processing localized at edge computing devices near the data sources rather than centralizing all data processing at remote data centers. This allows analytics to be performed where the data is generated, maintaining privacy by minimizing data transmission and storage at centralized locations, while still enabling comprehensive analytics through coordinated processing between edge devices and the data center.
Data Source
AI summary
An example computer system for using edge computing to enhance artificial intelligence and advanced analytics can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to: provide a data layer including a data source; use an edge computing layer to pre-process data from the data source; perform the artificial intelligence and advanced analytics on the data to form insights into the data; provide the insights to a data center or a cloud computing environment located remotely from the data source; and perform further artificial intelligence and advanced analytics on the insights at the data center or the cloud computing environment.