Monitoring System Event Analytics With Abnormality Timelines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing monitoring systems lack the ability to intelligently present security events in a way that allows users to easily understand the significance and abnormality of detected events over time.
Innovation Solution
An analysis engine that performs analytics on monitoring system data, weighting events based on importance and abnormality, and generates a graphical timeline with highlighted abnormal events, allowing users to view associated images and data at specific times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If security events are presented in a detailed list format, then complete information is provided, but user understanding of event significance and abnormality becomes difficult
Solution Approach 1:
The patent transforms one-dimensional event data into a two-dimensional visual representation by plotting event density against time on a graphical display. This dimensional transformation allows users to simultaneously perceive multiple events and their temporal relationships, making event significance and abnormality patterns visually apparent while preserving complete event information.
Solution Approach 2:
The patent employs color coding to differentiate between normal and abnormal events on the graphical display. By assigning distinct colors to events based on their abnormality classification, the system enables users to quickly identify significant occurrences without losing detailed event information, thus resolving the contradiction between information completeness and ease of understanding.
2Quantity of substance
If all security events are displayed equally, then data completeness is maintained, but identification of significant and unusual occurrences becomes difficult
Solution Approach 1:
The patent applies local quality by differentiating the visual representation of events based on their individual characteristics. Events are classified as normal or abnormal, and this classification is reflected in their graphical representation through color coding and positioning. This allows the system to maintain complete event data while providing precise visual cues about event significance and abnormality.
3Quantity of substance
If event density is increased to show more events, then data coverage is improved, but visual clarity and abnormality detection becomes harder
Solution Approach 1:
By introducing a visual dimension to event representation, the patent enables high event density to be displayed while maintaining clarity. The graphical display plots events along a time axis with vertical positioning indicating event density, allowing numerous events to be visualized simultaneously. Abnormal events are further distinguished through color coding, making them detectable even in high-density scenarios.
Data Source
AI summary
Techniques are described for presenting events detected by a monitoring system. A system receives a selection of a particular time period, identifies security events detected by a monitoring system during the selected time period, and classifies a subset of the identified security events as abnormal events. The system further generates a density of the identified security events over the selected time period, generates a density of the subset of the identified security events classified as abnormal events, and identifies monitoring system data associated with the selected time period. The system additionally generates a graphical representation of the density of the identified security events, the density of the subset of the identified security events classified as abnormal events, and the identified monitoring system data, and provides the graphical representation for display.