Anomaly Detection Grid for Software Installation Uniformity
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Solution Overview
Problem
System administrators face challenges in identifying and correcting anomalies in software package installations across multiple computers, as manual inspection of vast data sets is cumbersome and time-consuming, making it difficult to maintain uniformity and detect non-standard installations.
Innovation Solution
A method and system for anomaly detection and presentation that collects and displays installation information on a two-dimensional grid, sorting systems and installations based on their prevalence, with color-coding and interactive features to highlight anomalies and provide resolution information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If automated tools are used to maintain uniformity of package installations across computers, then installation uniformity is improved, but device complexity increases due to the need for additional automated tools and systems
Solution Approach 1:
The system automatically collects installation information from computers, generates anomaly reports, and provides corrective actions without requiring administrators to manually configure or manage the automated tools. The system serves itself by autonomously performing data collection, analysis, and report generation functions
Solution Approach 2:
The system provides feedback by generating anomaly reports that highlight configuration discrepancies and suggest corrective actions. This feedback loop enables administrators to maintain uniformity by addressing identified anomalies, thereby improving installation consistency across the computer group
2Device complexity
If manual inspection is used to identify configuration anomalies across computers, then device complexity is reduced, but loss of time increases due to the tremendous amount of data to manually analyze
Solution Approach 1:
The system replaces the mechanical manual inspection process with an automated computer-based system that collects installation information, analyzes configurations, and generates anomaly reports. This substitution eliminates the time-consuming manual data analysis while keeping the overall system architecture simple
Solution Approach 2:
The system acts as an intermediary between the computers and administrators by automatically collecting installation data, analyzing it for anomalies, and presenting findings in structured reports. This intermediary function saves administrators time by performing the complex data analysis task automatically
3Measurement precision
If comprehensive installation information is collected from all computers, then measurement precision is improved for anomaly detection, but loss of information increases due to the overwhelming amount of data to maintain
Solution Approach 1:
The system extracts only the relevant installation information needed for anomaly detection from the computers. By collecting and analyzing specific configuration data rather than maintaining all possible information, the system achieves precise anomaly detection while avoiding the burden of managing overwhelming amounts of data
4Manufacturing precision
If administrators manually correct anomalies by analyzing vast data sets, then manufacturing precision is improved for configuration uniformity, but productivity decreases due to the time required to survey and correct each anomaly
Solution Approach 1:
The system performs preliminary actions by automatically collecting installation information and generating anomaly reports with suggested corrective actions before administrators need to intervene. This preliminary analysis enables administrators to quickly implement corrections based on pre-prepared recommendations, improving both precision and productivity
Data Source
AI summary
A system and method for anomaly detection and presentation. The method of anomaly detection and presentation comprises receiving information for a plurality of traits from a plurality of servers. A first server has fewer of the plurality of traits than a second server. A first trait is on fewer of the plurality of servers than a second trait. The plurality of servers is rendered in a graphical display wherein the first server is positioned to one side of the second server based on respective numbers of traits had by the first and second servers. The first trait is rendered in the graphical display to one side of the second trait based on respective numbers of systems having the first and second traits. A table may be displayed in a cell in response to a user request. Anomalous traits may be displayed in an anomaly table.


