Numerous algorithms devised over the past decades efficiently compress text data.
在过去几十年内，制定的许多算法都能够高效地压缩文本数据。
To search vast quantities of text data, consider building and using a Sphinx index.
要搜索大量文本数据，请考虑构建和使用Sphinx索引。
Indexing is a process of converting text data into a format that facilitates rapid searching.
编制索引是将文本数据转换为有利于快速搜索的格式。
Analysis converts text data into tokens, and these tokens are added as terms in the Lucene index.
分析将文本数据转换为标记，这些标记将作为项添加到Lucene索引中。
Analysis is converting the text data into a fundamental unit of searching, which is called as term.
分析是将文本数据转换为搜索基本单位(称为项(term))的过程。
Returns a string containing the text data on the Clipboard.
传回字串，其中包含剪贴簿上的文字资料。
If you have text data to output, use a text display control and not a text edit control.
如果你要输出文本数据，则使用文本显示控件，而不要使用文本编辑控件。
OmniFind supports indexing text data in stream file objects, using an IFS path to identify the file.
OmniFind支持索引流文件对象中的文本数据，它使用IFS路径来标识文件。
WebSphere QualityStage supports the standardization, enrichment, and matching of freeform text data.
WebSphereQualityStage支持自由格式文本数据的标准化、充实和匹配。
This file contains plain text data, but will be shredded and removed after the encryption is complete.
这个文件包含纯文本数据，但这些数据将在加密完成后被分解并删除。
Before the text data is added to the index, it is processed by an analyzer (using an analysis process).
在将文本数据添加到索引前，由分析程序(使用分析过程)进行处理。
This paper proposes a rough spectral clustering algorithm and apply the algorithm on text data mining.
该文提出了一种粗糙谱聚类算法，并将其应用于文本数据挖掘。
Spool files with SNA Character Stream (SCS) data contain printer output that is formatted as EBCDIC text data.
含有 SNA字符流 (SCS)数据的spool文件包含EBCDIC 编码的文本数据，用于打印机输出。
With a few adjustments, the code here could perform other functions such as retrieving the text data from the object.
只需稍作修改，这个代码就可用于执行其他任务，比如从对象中检索文本数据。
CBC is a mode of cryptographic operation in which the plain text data is divided into chunks of equally sized data blocks.
CBC是一种加密操作模式，其中纯文本数据分为同样大小的数据块。
At the end, you concatenate all of the cipher text blocks to arrive at the cipher text form of the complete plain text data.
最后，串接所有密文块以得到全部纯文本数据的密文形式。
The update processing will determine which objects are new or changed on the system and index the text data for those objects.
更新处理过程会确定系统上的哪些对象是新添加的对象或是已经被修改了，然后索引那些对象的文本数据。
When this happens, you can assist Eclipse in classifying what kind of content is in the file - either text data or binary data.
当发生这种情况时，您可以帮助Eclipse对文件中的内容进行分类——是文本数据，还是二进制数据。
Combined with semantic similarity of text data, this paper gives a method of text data clustering based on semantic density.
结合文本数据的语义相似度，给出一种基于语义密度文本数据聚类的方法。
VARCHAR Stores single-byte or multibyte text strings of varying length (up to 255 bytes); supports code-set order collation of text data.
VARCHAR存储可变长度的单字节或多字节文本字符串(最高可达255个字节)；在整理文本数据时支持使用代码集顺序。
CHAR Stores single-byte or multibyte text strings of fixed length (up to 32,767 bytes); supports code-set order in collation of text data.
CHAR存储固定长度的单字节或多字节的文本字符串(最高可达32,767个字节)；在整理文本数据时支持使用代码集顺序。
Fortunately, starting in IBM I 7.1, a solution exists that allows us to index and search the text data associated with these IBM I objects.
幸运的是，从IBMi 7.1开始，就有一个解决方案允许我们索引和搜索与这些IBM i对象相关的文本数据。
Automatic text categorization, which is based on artificial intelligence technology, is the base of how to effectively utilize e-form text data.
基于人工智能技术的自动文本分类是有效利用电子化文本数据的基础。
With the rapid growth of online digital text data, text categorization has become one of the key techniques for handling and organizing text data.
随着在线文本文档数量的快速增长，文本分类已经成为处理和组织文本数据的一种关键技术。
Most of the metadata fields are not required; however, the content type is required if you intend to push non-text data such as PDF files or Word documents.
大多数的元数据字段不是必需的；但是，如果您打算推非文本数据（比如PDF文件或 Word 文档），内容类型是必需的。
For text data such as name, address, and product descriptions, a review of the varying data formats is critical to identify fields containing multiple domains.
对于姓名、地址和产品描述等文本数据，查看可变的数据格式是识别包含多个域的字段的关键。
Rather than sequentially searching through the text data using string matching, as done with the XQuery contains function, Net search Extender USES a text index.
NetSearchExtender并不使用字符串匹配连续地搜索文本数据(就像XQuerycontains函数所做的那样)，而是使用文本索引。
Using a text index makes document search much more efficient, as the text data does not need to be examined at query time like it is done with sub-string matching.
使用文本索引使文档搜索的效率大大提高，因为不需要像进行子字符串匹配时那样在查询时检查文本数据。
In order to be able to efficiently search full-text data, information about the individual words making up the textual content of the data and their location is needed.
为了能够高效地搜索全文数据，需要关于组成数据文本内容的各个单词及其位置的信息。
In order to be able to efficiently search full-text data, information about the individual words making up the textual content of the data and their location is needed.
为了能够高效地搜索全文数据，需要关于组成数据文本内容的各个单词及其位置的信息。
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