IT Asset Grouping via Cluster Analysis
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Solution Overview
Problem
Manual grouping of IT assets in large enterprises is laborious and prone to errors, becoming infeasible with tens of thousands of assets, leading to suboptimal groupings due to the complexity and reliance on administrator-defined criteria.
Innovation Solution
Implementing cluster analysis on configuration data to automatically generate and suggest logical groupings of IT assets, allowing administrators to accept or reject proposed groups, thereby reducing manual effort and improving grouping accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual grouping methods are used for IT assets, then administrators can create logical groups based on organization-specific criteria, but the process becomes laborious and error-prone when managing tens of thousands of assets
Solution Approach 1:
The system performs self-service by automatically discovering IT assets and generating logical groupings through cluster analysis without requiring administrator intervention. The system autonomously analyzes configuration data, identifies patterns, and creates suggested groups, freeing administrators from manual grouping tasks while maintaining high accuracy through algorithmic analysis of asset attributes.
Solution Approach 2:
The patent replaces the mechanical manual process of group definition with an automated computational system. Instead of administrators manually examining and grouping assets, the system uses cluster analysis algorithms to automatically process configuration data and generate logical groupings, substituting human effort with machine-based automated analysis.
2Adaptability or versatility
If administrators manually define grouping criteria, then groups can be created based on known attributes, but other useful groupings may be missed due to lack of awareness of key similarity attributes
Solution Approach 1:
The system performs preliminary action by conducting comprehensive cluster analysis on all asset configuration data before administrators need to define groups. This pre-analysis discovers potential grouping patterns and relationships that administrators might not anticipate, preparing suggested groupings in advance that reflect underlying similarities in the data that would otherwise remain hidden.
Solution Approach 2:
The patent introduces another dimension by analyzing assets across multiple configuration attributes simultaneously through cluster analysis. Instead of relying on administrators to consider one or two obvious grouping criteria, the system examines assets across many dimensions of configuration data, discovering relationships and patterns that exist in the multi-dimensional attribute space but are not immediately apparent to human administrators.
3Productivity
If automated population rules are used based on IT asset attributes, then group creation is faster, but correctness depends on discovering and updating the right metadata
Solution Approach 1:
The system implements feedback by presenting suggested groupings to administrators for review and validation. Administrators can examine the algorithmically generated groups, verify their correctness against organizational requirements, and provide feedback by accepting or rejecting suggestions. This feedback loop ensures that automated speed does not compromise correctness, as human expertise validates the results.
Solution Approach 2:
The patent introduces an intermediary role where administrators act as mediators between the automated cluster analysis system and the final group definitions. Rather than directly implementing automated rules or entirely manual processes, the system positions suggested groupings as intermediate recommendations that administrators review and approve, combining automated efficiency with human judgment to ensure correctness.
Data Source
AI summary
An information technology (IT) asset management system provides for logically grouping IT assets and performing actions on the logical groups. Cluster analysis techniques are used to analyze the configuration data corresponding to IT assets in the IT asset management system, generating proposed logical groups from the clusters determined by the cluster analysis techniques. A system administrator may be allowed to accept or reject the proposed logical groups.