IoT Pipeline Graph Completion via Virtual Gap Nodes
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Solution Overview
Problem
In IoT networks, the dynamic addition and removal of sensors create challenges in manually writing data pipelines to match the data format of raw sensor streams with the expected format of AI models, making it difficult to process data for analytics.
Innovation Solution
A computer-implemented method using a deep learning-based sequence model generates an initial data pipeline, identifies mismatches, and adds virtual gap nodes to correct format discrepancies, determining tentative graph structures and scores to finalize the pipeline with the help of a crowd-sourced validation system and incentive program.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual pipeline writing is used to match sensor stream formats with AI model formats, then data processing accuracy can be maintained, but the complexity and time required increases significantly in dynamic IoT environments
Solution Approach 1:
The system performs preliminary actions by automatically generating initial pipeline graphs using deep learning sequence models before manual refinement. The model pre-processes sensor stream data and predicts appropriate pipeline structures, transforming operations, and node configurations, thereby reducing the time required for manual pipeline development while maintaining format matching accuracy.
2Reliability
If manual pipeline writing is used to handle dynamic sensor additions and removals, then data processing reliability can be maintained, but the effort and complexity increase significantly
Solution Approach 1:
The system applies dynamics by enabling automatic pipeline graph generation and updating based on real-time sensor stream characteristics. When sensors are added or removed, the deep learning model dynamically regenerates the pipeline graph to match the new data formats, eliminating the need for manual pipeline management and reducing operational complexity while maintaining reliability.
Solution Approach 2:
The system implements self-service through autonomous pipeline graph generation and validation. The deep learning sequence model automatically analyzes sensor stream formats, generates appropriate pipeline structures, and validates the outputs without human intervention. This self-service capability handles dynamic sensor changes autonomously, reducing management complexity while ensuring data processing reliability.
3Productivity
If automated pipeline generation is used to reduce manual effort, then productivity improves, but the precision of format matching may deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the generated pipeline graphs are validated against expected output formats, and the validation results are fed back to refine the deep learning model. This iterative feedback process ensures that automated pipeline generation maintains high format matching accuracy while improving productivity through automation.
Solution Approach 2:
The system replaces manual mechanical pipeline writing with automated deep learning-based generation. The neural network model learns from training data to automatically generate pipeline graphs that match sensor stream formats with AI model expectations, achieving both high productivity through automation and maintained precision through learned patterns from training data.
Data Source
AI summary
An approach is provided for completing a pipeline graph. Using a deep learning based sequence model, an initial data pipeline having a sequence of nodes is generated. Mismatch(es) between data formats required by input and output in the sequence of nodes is identified. Virtual gap node(s) that correct the mismatch(es) are added to the initial data pipeline. For a given virtual gap node, tentative graph structures are determined using knowledge graphs and a crowd sourced validation system. Reuse forecast scores and performance scores for the tentative graph structures are calculated. Based on the reuse forecast scores and the performance scores, a final graph structure for implementing the given virtual gap node is determined.