Service Management Device Error Impact Visualization
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Solution Overview
Problem
Users face difficulty in determining the degree of influence of errors on service provision in computer networks due to changes in access frequency, as other factors besides service errors also affect access frequency, making it challenging to isolate the impact of errors solely from available information.
Innovation Solution
A service management device and method that graphically displays access frequency and predicted access frequency on a time series graph, along with error occurrence time, allowing users to easily assess the impact of errors on service provision by comparing actual and predicted access frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access frequency data is used to detect service errors, then error detection capability is improved, but measurement precision deteriorates because other factors also affect access frequency
Solution Approach 1:
The patent introduces a predicted access frequency as an intermediary element. Instead of directly measuring access frequency changes as error indicators, the system compares actual access frequency against predicted access frequency (derived from historical patterns). This intermediary comparison mechanism filters out other factors affecting access frequency, allowing precise measurement of error impact.
Solution Approach 2:
The system implements feedback by continuously comparing actual access frequency data with predicted access frequency data. The difference between these two values provides feedback information about service errors, enabling precise measurement of error impact while compensating for other factors that may influence access frequency.
2Device complexity
If only access frequency change information is provided, then device complexity is reduced, but loss of information increases because causal factors cannot be identified
Solution Approach 1:
The predicted access frequency acts as an intermediary that preserves causal information. By comparing actual versus predicted access frequency, the system maintains the ability to identify causal factors (including service errors and other influencing factors) while presenting information in a simple graphical format that does not require complex analysis.
Solution Approach 2:
The system creates a copy of historical access frequency patterns (predicted access frequency) and overlays it with actual access frequency data. This copying approach allows users to visualize the difference between expected and actual behavior, preserving information about causal factors while maintaining simple presentation.
3Ease of operation
If access frequency data is displayed without prediction, then ease of operation is improved, but loss of information increases because error impact cannot be isolated
Solution Approach 1:
The patent merges actual access frequency data with predicted access frequency data in a unified graphical display. This combination allows users to easily view both data types simultaneously while the visual comparison automatically isolates error impact, maintaining ease of operation without losing information about causal factors.
Solution Approach 2:
The system uses different visual representations (such as different line styles or colors) to distinguish between actual and predicted access frequency data. This visual differentiation makes it easy for users to identify error impact at a glance while maintaining simple overall display structure.
Data Source
AI summary
Disclosed is a unit for allowing a user, when any error occurs in a service, to easily ascertain the degree of influence on the service provision due to the error. In order to solve the above-described problem, there is provided a service management device including a graphing unit that graphically displays, on an identical time series graph, an access frequency to a predetermined service provided on a computer network and a predicted access frequency to the service in a state where no error occurs in the service, and displays an error occurrence time, on the time series graph, when the error occurs in the service.