Graph Data Flow Node Prediction via Completeness Scoring
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Solution Overview
Problem
Existing methods for automatically generating graph structures in ETL tools are inefficient, as they rely on breadth-first order processing, which can lead to incorrect prediction results when the prediction order is incorrect.
Innovation Solution
The proposed solution involves determining the next node to be predicted in a data flow based on past flow metadata and completeness, allowing for correct node prediction regardless of user input order.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If breadth-first order processing is used for automatic generation of graph structure, then the generation process can be simplified, but the prediction accuracy deteriorates when the prediction order is incorrect
Solution Approach 1:
The patent changes the processing parameter from fixed breadth-first order to dynamic order based on data flow metadata and completeness indicators. The system calculates completeness scores for different nodes and processes nodes in descending order of completeness, allowing flexible adaptation to different data flow scenarios while maintaining high prediction accuracy.
Solution Approach 2:
The patent introduces dynamic node selection based on completeness calculations. Instead of static breadth-first processing, the system dynamically determines which node to process next by evaluating completeness metrics, making the generation process adaptive to the specific data flow context and improving prediction accuracy.
2Productivity
If incorrect prediction order is used, then processing can continue without strict ordering constraints, but the correctness of prediction results deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by calculating completeness indicators for each node based on past flow metadata and current data flow state. This feedback information guides the selection of the next node to process, ensuring that nodes are processed in an order that maximizes prediction correctness while maintaining processing efficiency.
Solution Approach 2:
The patent performs preliminary calculations of completeness indicators for all nodes before actual processing begins. This preliminary action allows the system to determine the optimal processing order in advance, ensuring correctness from the start while maintaining efficient processing throughout the data flow creation.
3Ease of operation
If traditional auto-completion methods are applied to ETL tools, then user input support can be improved, but the complexity of automatically generating correct graph structure increases
Solution Approach 1:
The patent enables the system to automatically determine the correct processing order and select the next node to process based on intrinsic data flow metadata and completeness indicators. This self-service capability reduces the need for complex external control mechanisms while maintaining ease of operation for users.
Solution Approach 2:
The patent calculates completeness indicators for all possible next nodes (excessive action) rather than just the apparent next node. This comprehensive approach simplifies the overall system by pre-evaluating all options, making the final selection straightforward and reducing operational complexity.
Data Source
AI summary
Example implementations described herein are directed to systems and methods for selecting a prediction target node in creating a data flow. Systems and methods can involve a data flow editing interface, configured to, for receipt of an input node for an editing data flow in the data flow editing interface, determine a recommendation of a target node and a next node to be added to the data flow based on metadata, relevance of a data source for the data flow, location information of each node on the data flow, and similarity of the editing data flow to past flows that are previously executed data flows; and provide the recommendation of the target node and the next node on the data flow editing interface.