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US20170039554A1 Method And System For Real-Time, Load-Driven Multidimensional And Hierarchical Classification Of Monitored Transaction Executions For Visualization And Analysis Tasks Like Statistical Anomaly Detection 审中-公开
用于可视化和分析任务的监视事务执行的实时，负载驱动的多维和分层分类的方法和系统像统计异常检测

  • Patent Title: Method And System For Real-Time, Load-Driven Multidimensional And Hierarchical Classification Of Monitored Transaction Executions For Visualization And Analysis Tasks Like Statistical Anomaly Detection
  • Patent Title (中): 用于可视化和分析任务的监视事务执行的实时，负载驱动的多维和分层分类的方法和系统像统计异常检测
  • Application No.: US15227029
    Application Date: 2016-08-03
  • Publication No.: US20170039554A1
    Publication Date: 2017-02-09
  • Inventor: Bernd GREIFENEDER , Otmar ERTL , Herwig MOSER , Ernst AMBICHL , Helmut SPIEGL
  • Applicant: Dynatrace LLC
  • Main IPC: G06Q20/38
  • IPC: G06Q20/38 ;  G06Q20/10
Method And System For Real-Time, Load-Driven Multidimensional And Hierarchical Classification Of Monitored Transaction Executions For Visualization And Analysis Tasks Like Statistical Anomaly Detection
Abstract:
A system and method is disclosed that analyzes a set of historic transaction traces to identify an optimized set of transaction clusters with the highest transaction frequency. The transaction clusters are defined according to multiple parameters describing the execution context of the analyzed transactions. The transaction clusters are described by coordinates in a multidimensional, hierarchical classification space. Descriptive statistical data is extracted from historic transactions corresponding to previously identified transaction clusters and stored as reference data. Transaction trace data from currently executed transactions is analyzed to find a best matching historic transaction cluster. The current transaction traces are grouped according to their corresponding historic transaction cluster. Statistical data is extracted from those groups of current transaction trace and statistical test are performed that compare current and historic data on a per historic transaction cluster basis to identify deviations in performance and functional behavior of current and historic transactions.
Abstract(Chinese):

公开了一种系统和方法，其分析一组历史事务跟踪以识别具有最高事务频率的优化的事务集群。 根据描述分析的事务的执行上下文的多个参数来定义事务集群。 事务簇由多维分层分类空间中的坐标描述。 描述性统计数据是从与先前识别的事务簇对应的历史事务中提取出来的，并作为参考数据存储。 分析来自当前执行的事务的事务跟踪数据，以找到最佳匹配的历史事务集群。 当前事务跟踪根据其对应的历史事务集群进行分组。 从当前事务跟踪的那些组提取统计数据，并且进行统计测试，以比较每个历史事务簇的当前和历史数据，以识别当前和历史事务的性能和功能行为的偏差。

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