This value stream map illustrates the process of a patient coming into the hospital with a stomach ache.
这张价值流程图描述的是一名胃痛病人进入医院后的就诊流程。
Let's consider the very basic sample Value Stream Map used at the beginning of this article.
让我们考虑一下在这篇文章开始部分使用的价值流图的基本范例。
But first things first: What a Value Stream Map is and how one can be intelligently produced.
但是首先要指出的是，什么是价值流图，以及怎样去制作这样一个价值流图。
Beyond this basic example, let's consider some of the elements that could potentially go into creating a Value Stream Map.
超出这个基本的范例，让我们考虑一下创建价值流图所涉及到的一些基本元素。
If your Value Stream Map reveals long test "cycles" in the end game, it's very likely that you are guaranteeing wastefulness.
如果您的价值流图揭示了长期测试的“循环”，那么就能保证其中无效率内容的存在了。
Optional: A "what if?" alternative can be created (a second such picture, sometimes referred to as a "future state" Value Stream Map).
可选的：可以创建一个“如果这样做会怎样”的选择（第二个这样的图片，有时也叫做“未来状态”价值流图）。
A value stream map is a precise living document that allows us to see and understand the flow of information and material as a product or service moves through the value stream.
价值流图是精确且灵活的文件，让我们了解我们的产品或服务在价值流中流动的物料流与信息流。
The examples and concepts that follow are based on applying a Value Stream Map to a software engineering organization, but these concepts are applicable to a wide range of Settings.
接下来的例子和概念，基于一个范例，那就是对软件工程机构应用一个价值流图，但是这些概念可以应用于广泛的环境之中。
For example, it may be the case that your team is reviewing a Value Stream Map to understand how long it takes to implement a new requirement that comes from an external customer source.
例如，可能您的团队会评审一个价值流图，以理解实施一个外部客户源的新需求需要多长的时间。
Even if your team does reserve a long or late period for system test, it is important in a Value Stream Map to distinguish between the time spent doing actual testing and the time fixing code.
就算团队为系统测试保留了很长或者晚期的时间，那么在价值流图中区分花在实际测试与花在代码修复上的时间就非常的重要了。
MapReduce breaks down a problem into millions of parallel computations in the Map phase, producing as its output a stream of key-value pairs.
MapReduce在映射阶段将一个问题分解为数百万个并行计算，并生成键-值对流作为输出。
MapReduce breaks down a problem into millions of parallel computations in the Map phase, producing as its output a stream of key-value pairs.
MapReduce在映射阶段将一个问题分解为数百万个并行计算，并生成键-值对流作为输出。
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