Metadata Graph Path Finding for Exponential Search Reduction
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Solution Overview
Problem
Existing graph algorithms that find all paths from a source to a destination face computational challenges due to an exponential number of simple path permutations, leading to inefficient processing time and resource utilization.
Innovation Solution
A metadata-based approach is employed to generate a reduced graph, utilizing metadata to filter and traverse only relevant paths, reducing the graph complexity and optimizing the find path algorithm by employing metadata graphs and optimization algorithms to exclude unnecessary calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional find path algorithm traverses all possible paths in the graph, then all paths from source to destination are found, but the computation time increases exponentially due to the large number of path permutations
Solution Approach 1:
The graph is segmented into multiple data sets, with each data set represented by a reduced vertex in the metadata graph. This segmentation allows the algorithm to operate at the data set level rather than individual vertex level, reducing the search space from exponential to polynomial complexity while maintaining path finding completeness through metadata-based path reconstruction
Solution Approach 2:
A metadata graph is introduced as an intermediary structure between the original graph and the path finding algorithm. The metadata graph contains reduced vertices representing data sets and edge types representing relationships between data sets. This intermediary allows efficient filtering and path identification without traversing all original graph paths, resolving the contradiction between completeness and computation time
2Productivity
If the graph is reduced using metadata to fewer vertices, then computation time is reduced, but the complexity of generating and managing the metadata graph increases
Solution Approach 1:
The metadata graph structure serves multiple functions simultaneously: it represents the original graph's connectivity, enables efficient path finding, and provides a framework for filtering irrelevant paths. By making the metadata structure multi-functional, the patent avoids the need for separate complex structures, thereby improving productivity without proportionally increasing complexity
Solution Approach 2:
The patent changes the parameter of graph representation from individual vertices to data set aggregations (reduced vertices). This parameter change simplifies the graph structure for path finding purposes while the metadata maintains necessary detail information. The transformation from fine-grained to coarse-grained representation reduces computational complexity while preserving essential path information through metadata
Data Source
AI summary
A computer-implemented method for traversing a graph comprising: generating a metadata graph by reducing the graph, wherein the metadata graph is generated based on metadata describing one or more vertices and one or more edges of the graph. The metadata graph can comprise one or more edge types and one or more reduced vertices. Edge types in the metadata graph can be filtered to optimize finding of a path between a source vertex and a target vertex within a predetermined maximum path length.