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Horizontal axis (x): This axis represents the flow time intervals.
Vertical axis (y): This axis represents the frequency of occurrence of different flow times. The higher the bar, the more frequent the corresponding flow time was.
Histogram shape:
Focused to the left (short flow times): If the majority of bars are on the left of the histogram, this means that most events or transactions are processed quickly, which is generally a sign of a high-performance system.
Extended to the right (long flow times): If the histogram shows large bars to the right, this may indicate that some processes are taking much longer than expected, and could signal performance or overhead issues.
Bimodal or multimodal distribution: If the histogram shows multiple peaks, this could indicate that the system has varying performance depending on the type of transactions or execution period. For example, some quick transactions may go smoothly, while other, more complex transactions take longer.
Usefulness in
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Flow Analytics Pro
The Flow Time Histogram in Wiveez Flow Analytics Pro allows users to:
Visualize the distribution of response times in a system or application, to identify the proportion of processes processed quickly versus those that take longer.
Diagnose performance issues by detecting periods where flow times are high, allowing you to isolate potential causes such as resource overload, latency issues, or configuration errors.
Improve efficiency by adjusting processes or resources based on histogram observations. For example, if a spike in high flow times is detected at certain times, the user can decide to increase capacity or review processes to avoid significant delays.
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Of course ! The concept of Thin-Tailed and Fat-Tailed distributions is often used in risk analysis, statistics and finance to understand and model the impact of rare and extreme events. Here is a description that you could integrate into the Wiveez Flow Analytics Pro user documentation, adapted to explain their principle, their operation and their usefulness in this context.
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Thin-Tailed: A thin-tailed distribution is characterized by a relatively low probability that extreme events (or large deviations from the mean) will occur. In other words, extreme values (very far from the average) are rare. A typical example would be the normal (or Gaussian) distribution, where most of the data concentrates around the mean and extremes are very unlikely.
Fat-Tailed: In contrast, a fat-tailed distribution has a higher probability of extreme events. This means that rare (but very impactful) events are more frequent than would be expected with a thin-tailed distribution. Fat-tailed distributions are used to model phenomena where extreme events have a disproportionate impact, such as stock market crashes or economic crises.
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Detailed ticket analysis
Wiveez Flow Analytics Pro allows the user to analyze the performance of each flow in detail by displaying the list of tickets associated with a column and displaying the details of the Flow Metrics of a ticket.
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Analyze with our AI Alice
Wiveez Flow Analytics Pro provides you with its AI, named Alice, to help you analyze graphs.
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