Inference Techniques for Change Data Clustering
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Solution Overview
Problem
Compliance assessment reports generated by monitoring systems often become overwhelming due to numerous instances of changes, making it difficult for users to identify the cause and determine compliance with policies.
Innovation Solution
Implementing inference techniques and statistical methods to cluster and categorize change data and test results, allowing users to analyze fewer categories related to common events, such as software installations, rather than thousands of individual changes or test results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the monitoring system collects and reports all individual changes, then the completeness of change information is improved, but the complexity and usability of the report deteriorates
Solution Approach 1:
The patent merges multiple individual change occurrences into clustered categories based on common events. Changes are grouped by similarity and relationship, combining numerous individual change records into a smaller number of meaningful categories that represent underlying common causes, thereby reducing report complexity while preserving essential information.
Solution Approach 2:
The patent segments the comprehensive change information into distinct clustered categories. By dividing the large set of individual changes into organized groups based on common characteristics and relationships, the system maintains information completeness while improving usability through structured segmentation.
2Manufacturing precision
If the monitoring system reports all individual changes, then the detail level of change data is improved, but the time required to analyze compliance deteriorates
Solution Approach 1:
The system merges individual change data points into clustered categories that preserve detail through structured grouping. By combining related changes into categories with descriptive identifiers, the system maintains the necessary detail level while significantly reducing the time required to analyze compliance, as users can assess categories rather than individual changes.
Solution Approach 2:
The system performs preliminary clustering and categorization of changes before compliance analysis. By pre-organizing change data into meaningful categories based on common events and relationships, the system prepares the information in advance, reducing the time needed for subsequent compliance analysis while preserving detail through the structured category framework.
3Measurement precision
If the monitoring system provides comprehensive change reports, then the accuracy of compliance assessment is improved, but the usability for end users deteriorates
Solution Approach 1:
The system merges individual changes into categorized clusters that maintain assessment accuracy through structured grouping. By combining related changes into categories with meaningful identifiers and descriptions, the system preserves the accuracy needed for compliance assessment while dramatically improving usability, allowing users to efficiently evaluate compliance through categories rather than overwhelming lists of individual changes.
Data Source
AI summary
Methods, systems, and articles for receiving, by a monitor server, change data associated with a change captured on a target host, are described herein. In various embodiments, the target host may have provided the change data in response to detecting the change, and the change data may include one or more rules, settings, and/or parameters. Further, in some embodiments, the monitor server may analyze the change data in order to group the change data into clusters. Once the change data have been classified as clusters, a report may be generated providing classification or categorization and cluster information for the various changes. In various embodiments, the generating may comprise generating a report to the target host and/or to an administrative user.