Alert Clustering and Visualization for IT Infrastructure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual examination and prioritization of large volumes of semi-structured and unstructured textual alerts and incidents in IT infrastructure are inefficient, lacking effective clustering and visualization methods to identify patterns and trends.
Innovation Solution
A data clustering and visualization system that performs tokenization, normalization, distance metric computation, and hierarchical clustering to group similar alerts and incidents, enabling intuitive visualization and business intelligence metric analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual examination and prioritization of alerts and incidents is performed, then accuracy in understanding alert nature can be maintained, but productivity and operational efficiency deteriorate due to the large volume of textual data
Solution Approach 1:
The patent replaces manual mechanical examination of alerts with an automated computational system that performs tokenization, normalization, distance metric computation, and hierarchical clustering. This substitution of human cognitive processing with algorithmic processing dramatically increases productivity while reducing time loss, as the system can process large volumes of semi-structured and unstructured alert data automatically without fatigue or delay.
Solution Approach 2:
The patent creates visual representations (copies) of clustered alert data that preserve the essential patterns and relationships. The visualization system generates graphical displays of clusters, allowing analysts to quickly comprehend alert patterns without manually examining each individual alert, thus maintaining understanding accuracy while dramatically improving processing throughput.
2Loss of information
If clustering algorithms are applied to group similar alerts, then patterns and trends become identifiable, but device complexity increases due to multiple processing steps
Solution Approach 1:
The patent segments the complex task of alert analysis into distinct modular processing steps: tokenization, normalization, distance metric computation, hierarchical clustering, and visualization. Each module handles a specific aspect of the problem, making the overall complex system more manageable and maintainable while effectively preserving alert pattern information through systematic processing.
Solution Approach 2:
The patent introduces intermediate data structures and representations, such as distance matrices and cluster groupings, that serve as mediators between the raw alert data and the final visual output. These intermediaries transform unstructured text into organized numerical representations that can be processed by clustering algorithms, preserving information while managing complexity through structured transformation.
3Measurement precision
If hierarchical clustering with distance metrics is performed, then alert similarity accuracy improves, but computational time and resource usage increase
Solution Approach 1:
The patent performs preliminary processing steps (tokenization and normalization) before the computationally intensive clustering operation. By pre-processing the text data into standardized token representations, the system prepares the data in a format that enables more efficient distance metric computation and clustering, reducing the overall computational time while maintaining measurement precision.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for clustering and visualizing textual data. A data clustering and visualization system clusters large volumes of semi-structured and unstructured textual data into categories. Each category can include a group of similar alerts and incidents. The categories are then graphically presented.