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。
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。
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？
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。
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。
This error is returned if GRUB was expecting to read a number and encountered bad data.
当GRUB预期读到一个数字但是碰到非法数据时返回此错误。
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。
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。
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。
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？
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。
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。
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。
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。
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。
The data identification and correction methods for single bad data and a group of bad data are detailed.
论述了单个节点的不良数据辩识及修正过程和多个节点构成的区域的不良数据辩识及修正过程。
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。
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。
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。
The problem about bad data detection and identification in distribution system state estimation is focused in this thesis.
本文着重分析了配电网状态估计中的不良数据检测与辨识的问题。
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。
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。
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。
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。
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。
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。
The power system state estimation involves network topology, network observability analysis, state estimation and detection and identification of bad data.
电力系统状态估计的内容包括：网络拓扑分析、网络可观测性分析、状态估计、状态估计潮流、不良数据检测和辨识等。
What you have to do is have the courage to say I have got to account for the bad data, what I think are bad data and the data that helped me build my theory.
你们要做的就是，鼓起勇气来说，我已经解释出了那些劣质的数据，和我认为是劣质的数据，还有那些帮助我建立理论的数据。
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。
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。
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