Flow Model Computation with Disconnected Graph Segmentation
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Solution Overview
Problem
Existing methods for flow model computations in data models with disconnected graphs require creating a single connected graph, leading to inefficient processing time and memory usage, especially when dealing with large datasets.
Innovation Solution
A method to determine a node traversal order using forward and reverse flow lists, allowing for iterative computation of computational parameter values without creating a fully connected graph, thereby reducing processing time and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If disconnected graphs are connected to create a single connected graph by adding dummy nodes and edges, then the data model can be traversed recursively, but processing time and memory usage increase significantly
Solution Approach 1:
The patent segments the data model into multiple disconnected graphs and processes each graph independently rather than creating a single connected graph. This segmentation allows the system to traverse and compute flow values for each disconnected graph separately, avoiding the overhead of connecting them with dummy nodes and edges, thereby reducing processing time and memory usage.
2Ease of operation
If disconnected graphs are connected to create a single connected graph by adding dummy nodes and edges, then the data model can be traversed recursively, but memory usage increases
Solution Approach 1:
The patent segments the data model into multiple disconnected graphs and processes each graph independently. This segmentation prevents the need to load and manage a single large connected graph in memory, reducing the total memory footprint by processing smaller disconnected graph components separately.
3Ease of operation
If disconnected graphs are connected to create a single connected graph, then graph management is unified, but the process becomes complex
Solution Approach 1:
The patent segments the data model into multiple disconnected graphs and processes each graph independently using the same traversal algorithms. This approach avoids the complexity of managing dummy nodes and edges required to connect disconnected graphs into a single unified structure, while still achieving effective graph management through consistent processing of each segment.
4Productivity
If direct processing of disconnected graphs is performed, then processing efficiency improves, but traversal algorithms must handle disconnected components
Solution Approach 1:
The patent segments the data model into multiple disconnected graphs and applies standard recursive traversal algorithms to each segment independently. This segmentation approach actually simplifies the traversal algorithm implementation compared to handling a single connected graph with dummy nodes, as each disconnected graph can be processed with straightforward depth-first or breadth-first search without special handling for artificial connections.
Data Source
AI summary
A computing device determines a node traversal order for computing a computational parameter value for each node of a data model of a system that includes a plurality of disconnected graphs. The data model represents a flow of a computational parameter value through the nodes from a source module to an end module. A flow list defines an order for selecting and iteratively processing each node to compute the computational parameter value in a single iteration through the flow list. Each node from the flow list is selected to compute a driver quantity for each node. Each node is selected from the flow list in a reverse order to compute a driver rate and the computational parameter value for each node. The driver quantity or the computational parameter value is output for each node to predict a performance of the system.