If we're using the wrong mask, that means we're either throwing out real data, which is bad, or we're keeping bad data, which is worse.
如果我们使用了错误的遮光板，这意味着我们要么丢弃了真实数据，这是不好的；或者我们保留了不良数据，则更糟。
Query parsing can be quite difficult because of the complexity of describing topics in relation to location, not to mention spelling mistakes, language ambiguity, and bad data.
查询解析可能相差甚远，因为描述与位置的关系很复杂，更何况还存在拼写错误、语言歧义和不良数据等。
Even better, if bad data is entered, the CCD records will allow you to see which rows were affected and easily back out or update the needed values correctly.
更棒的是，如果表中输入了坏的数据，CCD记录还允许您查看哪些行受到影响，让您很容易改变主意，或者正确地更新所需的值。
Strictly speaking, these projects represent a cost of bad data in addition to degradation of business performance.
严格地说，这些项目反映了除业务性能降低之外还有劣质数据的损失。
It doesn't matter how good the database software is — if you put bad data in a database, nothing else matters.
这与数据库软件的好坏无关，如果将坏数据放入数据库中，其他一切都不重要了。
There's a fitting analogy between the digital world of data and the physical environment we live in. Bad data is like trash: pollutants that infest the environment.
数据的数字世界和我们所处的自然环境之间有一个相似之处，劣质的数据就像是一堆垃圾：遍布于环境中的污染物。
To understand how to deal with bad data, you should get familiar with some of the basic problems you face.
要了解如何处理不良数据，应当熟悉一些需要面对的基本问题。
Most bad data is much less egregious, and is more often a result of typos, transposed tracks, and the like.
大多数坏数据并没有那么过份，通常是拼写错误、曲目位置颠倒之类的错误。
What if someone is deliberately introducing bad data in the hopes of breaching your program's security?
如果有人故意引入坏数据来破坏程序的安全性又该如何呢？
Conventions for addressing null values are also important, as is what to do when we receive bad data.
处理空值的约定也很重要，当我们收到坏数据时就这样做。
This flexibility, however, comes at a big cost — your system is more vulnerable to bad data.
然而，获得这种灵活性所付出的代价相当昂贵——您的系统会更容易受到坏数据的攻击。
Errors in forecasting can result from bad data, wrong assumptions or a faulty model.
预测的误差可能来自错误的数据、错误的假设或错误的模型。
If you are passing arrays or Pointers, then you REALLY better be watching for errors or bad data.
如果要传递数组或指针，那么最好小心错误和坏数据的出现。
One form of hacker attack even involves feeding bad data to ARP tables, a practice known as ARP poisoning.
黑客攻击的一种形式就包括修改恶意数据到ARP映射缓存，即所谓的ARP中毒。
Another problem may manifest itself with bad data coming from an important database query.
通过重要的数据库查询得到的错误数据可以证明存在其他问题。
And, even if the project is successful, and bad data is transformed to good data, the repository immediately starts to degrade.
而且，即使项目成功实施，错误的数据转化为良好的数据，存储库还会立即失去作用。
That's where SAX can really help: it allows you to receive possibly bad data or error conditions before the program halts processing and gives you a chance to make course corrections.
这就是SAX的真正用途所在：在程序中断处理过程并进行修复之前，允许接收可能错误的数据或者错误状态。
The drawback, however, is that bad data frames, as well as good frames, are sent to their destinations.
但缺点是错误数据帧及正确数据帧都被发送到了目的地。
Bad data structure initializations going out of bounds of the allocated memory.
数据结构不正确的初始化超出了分配内存的边界。
The root of the problem I experienced with health information systems is a very bad data model. Evidence supporting my claim includes these observations.
在我的经历中，医疗信息系统的根本问题在于一个极其糟糕的数据模型，证据就是以下这些观察结果。
Why can't Word recognize when it's received bad data and simply put up an error message?
Word为什么不能意识到它接收到了坏的数据，并发出一条错误信息呢？
In fuzz testing, you attack a program with random bad data (aka fuzz), then wait to see what breaks.
在模糊测试中，用随机坏数据(也称做fuzz)攻击一个程序，然后等着观察哪里遭到了破坏。
It doesn't matter how beautiful and elegant your Web application is, bad data security will bring your application to its knees.
就算编写出人类历史上最美妙、最优雅的Web应用程序也没有多大意义。糟糕的数据安全性将会使应用程序崩溃。
You can either reject bad data, or abort the entire program when you find unsavory data, depending on the PEDANTIC setting given at the time you initialize AppConfig.
根据初始化appconfig时给定的PEDANTIC设置，可以拒绝错误数据，或者在发现恶意数据时异常终止整个程序。
Initially, writing this code can be a little dreary, but it also has a real advantage in reliability: many mysterious problems instantly disappear if you immediately reject bad data.
在开始时，编写这些代码可能会有一点无聊，不过它对于可靠性确实有好处：如果您拒绝了那些非法数据，许多不可思议的问题马上就会消失。
Virtually all non-trivial problems require you to filter good data from bad.
几乎所有重要问题都需要从无用数据中过滤出有用数据。
Bad data detection and identification is one of the chief functions in the running state analysis of the distribution system.
不良数据检测与辨识是配电系统运行状态分析的主要功能之一。
Based on this, it is presented to a new method on bad data of distribution systems monitored and modified and a distribution state estimation based on matching power flow.
由此提出了一种新的配电系统不良数据检测及校正的方法，并给出了一种基于配电网匹配潮流状态估计方法。
Present status of the distribution system state estimation, and methods of bad data detection and identification are surveyed here.
并对配电网状态估计的研究现状和常用不良数据的检测与辨识方法进行了描述。
Lastly, several conclusions on bad data detection and identification for distribution system are given.
最后，得到了配电网络不良数据检测与辨识的若干结论。
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