Graph-Based Affinity Determination for Anomaly Resolution
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Solution Overview
Problem
Traditional techniques fail to accurately determine affinity among events in large dimensional spaces with multiple attributes and values, which hinders the identification of underlying causes of anomalies in complex systems.
Innovation Solution
Graph-based affinity determination techniques build a multi-planar graph with nodes representing attribute values and edges for co-occurrence, computing edge weights based on node and edge counts, and using shortest path algorithms to identify paths and determine affinity between attribute values, thereby resolving anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional techniques are used to determine affinity among events, then the process is simple, but the accuracy of determining affinity in large dimensional spaces deteriorates
Solution Approach 1:
The patent segments the complex affinity determination problem into distinct computational steps: constructing a graph representation of events and attributes, computing co-occurrence statistics, calculating affinity scores using the formula, and ranking results. This segmentation allows each step to be optimized independently, improving overall accuracy while managing computational complexity.
Solution Approach 2:
The patent transforms the affinity determination problem from traditional statistical analysis into a graph-theoretic framework, adding a new dimensional perspective. By representing events as nodes and attributes as edges in a graph structure, the system can process large dimensional spaces more effectively, achieving higher measurement precision without proportionally increasing complexity.
2Measurement precision
If graph-based affinity determination is used, then the accuracy of identifying event relationships improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing co-occurrence counts and storing them in a graph structure before affinity calculation. This preliminary processing organizes the data in a way that enables efficient affinity score computation, reducing the computational power needed during the actual affinity determination phase while maintaining high accuracy.
Solution Approach 2:
The patent changes the computational parameters by using a mathematically optimized affinity score formula that balances precision with computational efficiency. The formula incorporates normalization factors and weighting that allow accurate relationship identification while controlling resource consumption, effectively managing the trade-off between accuracy and computational power.
Data Source
AI summary
In some examples, a computer receives event data relating to events of a system, and builds a graph comprising nodes representing respective values of attributes of the events, and edges between the nodes, each respective edge of the edges representing co-occurrence of values of attributes represented by nodes connected by the respective edge. The computer computes edge weights of corresponding edges of the edges, and identifies paths in the graph, where each path of the paths comprises two or more nodes and a corresponding edge or edges. The computer determines an affinity among the values of the attributes based on a shortest path determining process that uses the edge weights for distances of the identified paths, and resolves an anomaly in the system based on the determined affinity.