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Systems and/or methods for statistical online analysis of large and potentially heterogeneous data sets

机译:用于大型和潜在异构数据集的统计在线分析的系统和/或方法

摘要

Certain example embodiments relate to using Complex Event Processing (CEP) techniques for statistical analysis of cache behavior and parameters, e.g., in connection with large, potentially heterogeneous data sets (e.g., “Big Data”). A dedicated stream mining operator registers a listener to a cache and receives notifications on cache operations. For selected element attributes, a first model estimates the probability density functions of the attribute values, delivering well-defined estimates of the attribute value distributions. A second model analyzes the time elements stay in the cache (“validity”). Validity is combined with the attribute value distribution. A meaningful analysis model (Cache Element Model) can be derived by combining additional summary statistics for the validity with the attribute value distribution, describing how long elements stay in the cache for attribute values of a specific region, and how the values are distributed. It may be used to inform administrative tasks such as, optimization of cache parameters.
机译:某些示例实施例涉及使用复杂事件处理(CEP)技术,例如结合大的,潜在地异构的数据集(例如,“大数据”)来对缓存行为和参数进行统计分析。专用的流挖掘运算符将侦听器注册到缓存,并接收有关缓存操作的通知。对于选定的元素属性,第一个模型估计属性值的概率密度函数,从而提供对属性值分布的明确定义的估计。第二个模型分析了保留在缓存中的时间元素(“有效性”)。有效性与属性值分布结合在一起。通过将有效性的附加摘要统计信息与属性值分布相结合,描述特定区域的属性值元素在缓存中停留的时间以及这些值的分布方式,可以得出有意义的分析模型(缓存元素模型)。它可用于通知管理任务,例如优化缓存参数。

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