Thus, it is more important to provide continuous data stream than to reduce the delay of single memory access.
因此，向处理器内核提供连续的数据流比降低单个存储访问的延迟更加重要。
In these applications, the data which called data stream is multidimensional, continuous, rapid, and changed with time.
在这些应用中数据采取的是多维的、连续的、快速的、随时间变化的流式数据的形式。
Data streams are continuous, fast, unlimited, unknown, so traditional technology of data mining is not suitable to data stream mining. Analysis and mining data stream has been a popular research.
数据流的连续、快速、无限、未知的特点决定了传统的数据挖掘技术已经不适合数据流挖掘，分析和挖掘数据流已经成为热点研究问题。
In addition, the business data stream is continuous, conflict, timing, massive and distributed, so traditional data mining techniques can not be applied directly to the business data stream.
而商业数据流除了具备数据流的基本特点外，还具备连续性、冲突性、时间性、海量性和分布性等特性。因此传统的数据挖掘技术不能直接应用到商业数据流上。
At certain time intervals, the user injects new data into the 'stream' which will be surrounded by a continuous stream of padding data.
在特定的时间间隔，用户注入新数据的“流”将被填充数据的连续流。
At certain time intervals, the user injects new data into the 'stream' which will be surrounded by a continuous stream of padding data.
在特定的时间间隔，用户注入新数据的“流”将被填充数据的连续流。
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