Pattern-Driven Dataflow Reactions for Real-Time Edge Analytics
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Solution Overview
Problem
Traditional enterprise software applications relying on cloud infrastructure face challenges with connectivity, bandwidth, latency, and cost when handling large volumes of data from industrial machines, leading to inefficiencies in real-time decision-making and predictive maintenance.
Innovation Solution
Implementing a dataflow graph with pattern matching capabilities in edge computing systems, enabling intelligent data processing and analytics at the source of IoT devices, using a platform like FogHorn to optimize resource usage and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is sent to cloud storage, then centralized data processing is achieved, but bandwidth requirements increase and latency varies
Solution Approach 1:
The patent segments the centralized cloud processing architecture into distributed edge computing nodes. Each edge device processes sensor data locally, dividing the monolithic cloud system into multiple autonomous segments that operate independently, thereby reducing the need for continuous bandwidth consumption while maintaining reliable real-time processing
Solution Approach 2:
The patent introduces edge computing devices as intermediary layers between sensors and cloud storage. These intermediaries process and filter data locally before transmitting only essential information to the cloud, acting as mediators that reduce bandwidth requirements while ensuring real-time decision-making capabilities at the edge
2Loss of time
If data is processed at the edge, then latency is reduced and real-time analytics are enabled, but device complexity increases
Solution Approach 1:
The patent implements universal edge computing platforms that can perform multiple functions including data collection, pattern recognition, predictive analytics, and local decision-making. This multi-functionality consolidates various processing tasks into single devices, reducing overall system complexity while enabling fast real-time responses
Solution Approach 2:
The patent enables edge devices to autonomously process and analyze sensor data without requiring constant cloud connectivity or manual intervention. The systems self-manage data processing, model training, and decision-making locally, reducing the operational complexity burden while achieving rapid response times
3Productivity
If cloud infrastructure is used, then system efficiency is improved, but connectivity requirements increase operational costs
Solution Approach 1:
The patent performs data processing, analysis, and pattern recognition in advance at the edge devices before data needs to be transmitted to the cloud. This preliminary action at the source eliminates the need for repeated cloud round-trips, maintaining high system efficiency while significantly reducing operational costs related to connectivity and cloud processing
Data Source
AI summary
A technique implements a dataflow graph, taking a number of streams of data inputs and transforms these inputs into a number of streams of outputs. The dataflow graph can perform pattern matching. The technique implements reactions via the composition of pattern matching across joined streams of input data. A completeness of matching an input sequence to a particular input pattern can be characterized as having at least three different degrees, such as cold (not yet matched), warm (e.g., minimally matched), and hot (e.g., maximally matched). The input pattern to be matched can have a variable length, including zero length or unlimited or arbitrarily large length. Data flows can be on a push basis or pull basis, or a combination, and may change depending on the state.