Cluster Identity Tracking via Anchor Node Propagation
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Solution Overview
Problem
Current clustering algorithms for network devices fail to effectively track the impact of incidents over time, as they lack a method to correlate and maintain cluster identities across different time steps, leading to undefined relationships between clusters.
Innovation Solution
A real-time stream-based clustering algorithm that assigns and propagates a unique identifier to clusters based on anchor nodes, allowing for the tracking of incidents as they change over time through merging, splitting, or remaining constant, using a decay function to associate time with incident frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional clustering algorithms are used to group network devices, then devices can be grouped into clusters based on similar attributes, but the relationships between clusters over time are not well-defined and tracked
Solution Approach 1:
The patent applies preliminary action by pre-assigning unique identifiers to clusters when they are first formed, and then propagating these identifiers across time steps. This allows cluster identities to be tracked without requiring complex retrospective analysis, as the tracking mechanism is established in advance through the identifier assignment and propagation process
Solution Approach 2:
The patent uses unique cluster identifiers as intermediary elements that mediate between different time steps and cluster configurations. These identifiers serve as persistent references that allow the system to track and relate clusters across time without directly comparing complex cluster attributes, simplifying the tracking mechanism
2Productivity
If cluster algorithms generate independent clusters at different time steps, then clustering can be performed independently, but the impact of incidents cannot be tracked over time
Solution Approach 1:
The patent ensures continuity of useful action by maintaining persistent cluster identifiers across time steps. This allows the tracking of incident impacts to continue uninterrupted over time, as the same identifier follows the cluster through various transformations and time transitions, enabling continuous monitoring without restarting analysis
3Reliability
If traditional clustering is used without identifier propagation, then simpler algorithms can be employed, but relationships between clusters at different times remain undefined
Solution Approach 1:
The patent applies universality by designing a cluster identifier system that serves multiple functions simultaneously: it uniquely identifies clusters, tracks their evolution over time, defines relationships between clusters at different time steps, and enables incident impact analysis. This multi-functional approach establishes reliable cluster relationships without proportionally increasing complexity
Data Source
AI summary
A real-time stream-based clustering algorithm is disclosed for correlating network impact according to time and space. The clustering algorithm operates at discrete time steps and produces a partitioning of a network graph such that each partition is a cluster. Clusters are tracked at each time step and the partitions can change by disappearing, splitting or merging with others. To track an incident over many clustering time steps, an ID is assigned to and related to previous clusters such that the same ID can propagate between multiple cluster time steps. Thus, a same incident can be tracked over time as its effect traverses the network. Anchor nodes can be assigned to the clusters to establish a relationship between clusters at different time steps.