如果有丢失的或脏数据，您将如何进行呢？
本例的“脏数据”包含40%的可疑副本。
The "dirty data" for the example includes 40% suspect duplicates.
它并不等于fsync函数——也不是请求同步脏数据。
It is not equivalent to fsync — it's not a request to sync dirty data.
除了上述问题之外，与“脏数据”相关的问题也总是存在。
Beyond the issues above are the ever present problems associated with "dirty data".
“脏数据”的生成方法与干净数据的生成方法类似。
It was generated using an approach similar to the approach used to generate the clean data.
这就是与干净数据相比，“脏数据” 的开销更小的原因所在。
This is why there is less overhead for dirty data as compared to clean data.
文中提出了基于改进ART2网络的脏数据辨识与调整模型。
A model for dirty data identification and justification is put forward based on the modified ART2 network.
脏数据处理的过程就是对于含有脏数据的负荷曲线模式的辨识过程。
The process of dirty data processing is the recognition process of load curve pattern containing dirty data.
如果无法在客户的数据源中纠正丢失的数据或脏数据，什么业务规则将用于纠正数据呢？
If the missing data or dirty data cannot be corrected in the customer? S data sources, what business rules will be used to correct the data?
注意在属性设置函数中设置一个同步变量的值不会使他的脏数据标志被设置。
Note that setting a SyncVar member variable inside a property setter function does not cause it to be dirtied.
数据库读写器(DBWr)——异步地将缓冲区的脏数据写到物理数据文件中。
Database Writer (DBWr) - Writes to physical data files the dirty bits of buffer asynchronously.
合并意味着如果相同的记录被更新，或者在缓冲区内被多次标记为脏数据，则只保证最后一次更新。
Conflation means if the same record is updated or dirtied multiple times within the buffering period then it only keeps the last update.
详细而准确的元数据对于数据仓库的创建、数据加载、运行维护、清理脏数据等工作都必不可少。
Detailed and exact metadata is absolutely necessarily for the creation, the data loading, the data cleaning and regular maintenance of a data warehouse.
高速缓存中包含脏数据，但由于某些虚拟磁盘丢失或将转为脱机，因此无法将高速缓存的数据写入磁盘。
The cache contains dirty data, but some virtual disks are missing or will go offline, so the cached data can not be written to disk.
随着时间的推移，劳动力市场信息系统产生了大量的“脏数据”，降低了数据准确性和系统服务的质量。
Some "Dirty data" will appear in labor market information system after some time. It affects data accuracy and quality of system services.
因为同步变量使用他们自己内部的标识记录脏数据状态，在属性设置函数中设置脏位会引起递归调用问题。
Because SyncVars use properties internally to mark themselves as dirty, setting them dirty inside property functions could lead to recursion problems.
一个写线程释放一个锁之后，另一个读线程随后获取了同一个锁。本质上，线程释放锁时会将强制刷新工作内存中的脏数据到主内存中，获取一个锁将强制线程装载(或重新装载)字段的值。
In essence, releasing a lock forces a flush of all writes from working memory employed by the thread, and acquiring a lock forces a (re) load of the values of accessible fields.
理想情况下，多个线程不能同时访问同一块数据，脏读将不复存在，死锁则会被自动监测和处理。
Ideally no two threads can try to modify the same piece of data at the same time, dirty reads are not possible, and deadlocks are automatically detected and handled.
如果有丢失数据或脏(dirty)数据，您的客户是否可以在数据源中进行纠正呢？
If there is any missing data or dirty data, can your customer correct this in the data sources?
脏读：A 1检索a 2未提交的数据。
相反，在缓存中一旦进行更新操作，缓存就会跟踪脏记录列表，并定期将当前的脏记录集刷新到数据库中。
Instead, updates occur in the cache, the cache tracks the list of dirty records and periodically flushes the current set of dirty records to the data store.
脏记录列表将使用大型的批事务写入到数据源中。
The dirty record list is written to the data source using a large batch transaction.
流作用域的持久化上下文对象将流期间加载的数据作为持久化实体来管理并将数据变更缓存为实体的脏状态。
The flow-scoped persistence-context object manages data loaded during the flow as persistent entities, and data changes cached as the entities' dirty states.
换句话说，将把数据缓冲区的所有脏位刷新到数据文件。
In other words, all dirty bits of the data buffer will be flushed to the data files.
SQL 2005有一个 基于快照的隔离级别 ，它能在不允许“脏读”的情况下避免数据读取阻塞数据写入，或者数据写入阻塞数据读取。
SQL 2005 has snapshot-based isolation levels that prevent readers from blocking writers or writers from blocking readers without allowing dirty reads.
注意，一些页面可以指向文件，在这种情况下，如果页面是脏(dirty)的，数据将被冲洗，如果页面是干净的(clean)，直接丢掉。
Note that some pages can refer to files, in which case, the data can be flushed if dirty (through the page cache) or, if the page is clean, simply discarded.
不过有一个例外，对于“脏(dirty)”表空间中的long型字段和大型对象数据，总是需要进行备份。
The exception is for long field and large object data in "dirty" table Spaces, which are always backed up.
如果记录是脏的(比如，和你的数据版本不同)，这时你中止事务，用户会重新启动它。
If the record is dirty (i. e. different version to yours), then you abort the transaction and the user can re-start it.
这些数据将初始化事务表、脏页表和打开文件表，以便它们能够用于恢复过程。
This data initializes the transaction table, dirty pages table, and open file table so they can be used in the recovery process.
对中国气象台站的数据资料进行统计，发现五脏与四时四方的对应关系。
The statistic on the climatic digits of China discover the corresponding relationships between the five Zang and the four seasons, the four directions.
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