Machine-Learning

Automatic Data Mining

  • Data Profiling

    XpoLog scans your data to build a unique signature pattern for each event.
    XpoLog has a unique profiling engine it uses when scanning your data. Using this profiling engine, XpoLog tags the severity level, sources, trends, and many other algorithms. This way, XpoLog creates a profile of the data and uses this profile for monitoring and mining. The profile is used when creating signature patterns for your events that are used for XpoLog’s automatic built-in log parsing. The Automated parsing knows how to extract which data to which column, which in turn allows for detailed analysis when searching through your logs.

  • Error Tagging

    When XpoLog displays your search results, each event automatically comes with a autodetected severity level.
    XpoLog has a unique built-in mechanism for understanding the severity level any anomaly has in any application. As a user, you can instantly tag or filter events. Hence, error tagging is essentially severity tagging. Analytics Insights give you these tags. XpoLog is not just popping up problems, but also allowing you to see instantly which errors you should tend to first. In addition, XpoLog gives you the option of changing any severity at any time. The anomalies are listed according to where they are found, according to when in the desired time span they were found, but also XpoLog Analytics gives you a list according to severity level, with the high severity first (red), then all those with medium priority problems (orange), and lastly, those containing low priority problems (green). In this list, XpoLog lists the most severe problems first.
    By default, XpoLog decides the severity level according to the highest severity anomaly found in the event. You may be searching for an anomaly with a medium severity, but if, in the same event, another anomaly with high severity is found, the event as a whole will be marked as high priority. Since XpoLog sets the severity record straight, you can set your priorities straight.

  • Semantic Processing

    XpoLog text analysis technology deep dives into each event looking for terms and keywords to determine the severity level by comparing it to our dictionary. By tagging events automatically according to their semantic meaning XpoLog builds better profiling technologies.
    Filtering the log data in order to find a specific data format, ID, problem, or other pieces of information is crucial in any data mining process. Semantic processing is the technology that allows for automatic error tagging. Every time XpoLog displays a search result, it automatically includes error tagging, not only for the errors you were searching for, but also for all the errors XpoLog found that you were not even aware of. XpoLog Semantic processing makes sure that even if you were just looking for a minor error, but a critical error was luring in the background, you will know about right away.

  • Log Messages Trends

    Use XpoLog’s results to find various logical structures that makes it easier to discover trends and issues.
    For each log event you search, XpoLog creates a unique ID (signature). XpoLog then uses these signatures to track and keep statistical trends and occurrence profiles.
    You will often experience that there are certain times when your applications experience more errors and more messages, it could be certain times of the day, certain days in the week, certain weeks per year… During these times.you will notice a clear increase or decrease in errors or number of users. There could be any number of reasons for these trends of increases and/or decreases, but to be able to analyse them you first need to know that they exist, and when.

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