stream time meaning in English
连续开工时间，工作周期。
Examples
- Ptwma is an effective successful algorithm and model to the knowledge discovery of the multiple streams time series
Ptwma为分布式，并行控掘多流时间序列提供了一种有效的算法和模型。 - 4 . research of online similar search in a streaming time series an algorithm on online similar search in a streaming time series is proposed
4 ）动态时间序列在线模式的相似性查找研究针对时间序列实时分析的需要，给出了一种动态序列的在线相似性查找算法。 - These researches will help us to discover changing or developing principle of things , support to decision - making , etc . the thesis addresses several key technical problems of pattern mining and its search based similarity in time series , which covers feature patterns and relationship patterns mining , pattern search based similarity in time series and stream time series and issues concerning application system implementation oriented to analysis . major contributions of this thesis include : 1 . research of mining feature patterns in time series a novel method is proposed to discovery frequent pattern from time series
本文在分析时间序列特点和实际应用需求的基础上，针对时间序列的挖掘与相似性查找一些关键技术进行了研究，具体包括特征模式挖掘、多序列关联模式挖掘、相似性模式查找等方面，所做的工作和取得的创新成果体现在以下三个方面： 1 )时间序列特征模式挖掘研究首次提出了一种基于互关联后继树模型的时序特征模式挖掘方法。 - We ca n ' t divide the multiple streams time series into singleness times series simply in the research of multiple streams time series , we ' ll dissever the relation between the events of the multiple streams . although the msdd can find the dependency relationship of multiple streams , but it have n ' t the initialization of the events , the express of the time relationship between events is not frank , the cost of the algorithm is expensive ( o ( n5 ) ) , i ca n ' t find much more knowledge in multiple time series , it find the dependency patterns only of the multiple time series , so there need a new more effective , frank , complete algorithm to find the knowledge
研究多流时序不能简单地将它割裂为单流时序，因为这样就割裂了数据流事件之间的关系。虽然msdd能够发现多流时间序列中的依赖模式，但是由于其缺少对数据的初始化、事件之间时间关系的表示不直观、算法执行的时间空间开销很大( o ( n ~ 5 ) ) 、不能够充分发现多流时间序列包含的知识，它只发现依赖关系，因此研究新的，高效，全面的发现多流时间序列事件之间关系的算法成为必要。本文分析了单一和多流时间序列中的知识发现，把多流时间序列事件内部存在的关系表示为：关联模式、依赖模式、突变模式。 - This paper analysis the data mining of the single nd multiple streams time series , and draw a conclusion that the relationship between the events of the multiple streams time series are the association patterns dependency patterns , sudden patterns , this paper call them are structure patterns , the existing algorithm have n ' t discuss these patterns , although msdd discussed the dependency patterns , however , it ignored the association patterns , sudden patterns , this paper have a definition of the association patterns , sudden patterns and dependency patterns , and have a complete , frank algorithm called twma ( time window moving and filtering algorithm ) , the peculiarity of this algorithm is that events is listed by the time window , by this way , the relationship of the events is clear
本文将它们统称为结构模式，而这正是目前其它算法、没有考虑到的，虽然msdd考虑了事件之间的依赖关系，但它忽略了突变模式，关联模式等重要的知识表示。本文给出了关联模式、依赖模式、突变模式的定义，提出了一个比较灵活全面、直观的挖掘它们的算法：时间窗口移动筛选算法twma ( timewindowmovingandfilteringalgorithm ) 。该算法的一个突出特点是将时间序列事件按时间窗口序列化，使得事件之间的时间关系表示很直观，该算法能成功地从多流时间序列中发现了事件之间的关系。