GPars还定义通过线程池(比如actors)调度的逻辑数据流任务并通过数据流变量进行传输。
GPars also defines logical dataflow tasks that are scheduled over a thread pool (like actors) and communicate via dataflow variables.
操作、销售和财务人员也因消除了任务关键数据流中的迟滞现象而获得了实时的决策支持。
Operations, Sales and Finance personnel also have real-time decision support by removing the latency in the flow of mission critical data.
在设计的过程中，关键的任务是指定从数据源到目标的数据流，即如何重建并将源模型合并到目标模型中。
The key task during design time is to specify the data flow from the sources to the target — that is, how to restructure and merge source models into the target model.
在这个特别的数据流中，MetadataWorkbench支持所有参与到其中的组件(任务、数据库、表和BI报告)。
In this particular data flow, Metadata Workbench supports all participating components (jobs, database tables, and BI reports).
在通过连接器收集了相应的数据之后，整合服务器的主要任务是根据数据流规范对数据进行处理。
After the data is gathered through the connectors, the core of the consolidation server processes the data according to the data-flow specification.
设计ETL任务(或数据流)是将XML集成到数据仓库环境的重要方面。
Designing ETL jobs (or data flows) is an important aspect of integrating XML into data warehouse environments.
后台任务将使用DataFlowStream来将结果tweets流式传输回主线程，该主线程从数据流中读取它们。
Background tasks will use the DataFlowStream to stream result tweets back to the main thread that reads them from the stream.
在可重构硬件资源约束条件下，将任务的数据流图划分成数个子模块。
The task data flow graph(DFG) was partitioned into several sub-modules under hardware resource constraint.
如何在资源有限的情况下，快速执行查询处理并最大限度地减少查询精度的损失是数据流查询处理的主要任务之一。
It is one of the major tasks to execute query timely with less performance and precise loss in a data stream system when the system resource is limited.
频繁项集挖掘是一个非常基本的，但最重要的任务，在数据流处理。
Frequent items mining is a very basic but important task in the data stream processing.
本文首先扩展了任务结构的表达能力，增加了对任务间数据流的支持；然后对扩展任务结构的合理性进行了分析和验证。
At first we extend the expressive power of the task structure such that it can support data flow between tasks, and then we analyze and verify the soundness of the extended task structure.
因此，针对仿真中常用的数据挖掘任务，研究时空效率高效的相应数据流挖掘算法具有重要意义。
Thus, it is important to research data stream mining algorithms having higher time and space efficiency, and to aim at resolving data mining tasks often used in system simulation.
截止期和关键性是这类应用中的两个突出特征，结合这些特征研究合适的数据流处理技术对提高关键任务型应用的处理效率是非常必要的。
The problem of deadline and critical are two major characteristics, so it is necessary to do research for a proper real-time processing strategy in order to improve query efficiency.
截止期和关键性是这类应用中的两个突出特征，结合这些特征研究合适的数据流处理技术对提高关键任务型应用的处理效率是非常必要的。
The problem of deadline and critical are two major characteristics, so it is necessary to do research for a proper real-time processing strategy in order to improve query efficiency.
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