What is the translation of " QUERIES WILL " in Chinese?

• 查询将

Examples of using Queries will in English and their translations into Chinese

Most queries will be answered here.
大多数查询将在这里得到解答。

The performance of several queries will now be analyzed.
现在将分析几个查询的性能。

All our queries will end up inside this type.
我们所有的查询都将以此类型结束。

Gartner predicts that by 2020 50% of analytical queries will be generated via search, NLP or voice.
Gartner预测，到2020年，50%的分析性查询将通过搜索、nlp或语音生成。

Hadoop queries will be channelled through the Apache Hive data warehouse software.
Hadoop查询将通过ApacheHive数据库仓库软件来通信。

A large proportion of the user queries will be directly answered or performed.
大部分用户的查询，将会得到直接的回答或执行。

Queries will be based on full questions or commands and the probability of using single keywords in that form of search is relatively low.
查询将基于完整的问题或者指令，而以单个关键词去搜索的概率相对来说比较低。

In this scenario, at least half of all data queries will be conducted through NLP or voice or automated tools.
在这样的情况下，至少有一半的数据查询将通过NLP、语音或自动化工具进行。

This BERT algorithm helps search engines to understand human natural speech and that means that long search queries will get more accurate results.
这种BERT算法可帮助搜索引擎理解人类的自然语音，这意味着长搜索查询将获得更准确的结果。

Most of your queries will get answered there itself.
大多数查询将在这里得到解答。

You can write new data with a heavier weight- the data will be distributed slightly unevenly, but queries will work correctly and efficiently.
你可以给新分片分配大权重然后写新数据-数据可能会稍分布不均，但查询会正确高效地运行。

All technical queries will be answered within 24 hours.
所有的技术问题将在24小时之内回答。

Gartner predicts by 2020 that 50 percent of analytical queries will be generated via search, NLP or voice.
Gartner预测，到2020年，50%的分析性查询将通过搜索、nlp或语音生成。

Fifty percent of analytic queries will be generated using search, natural-language processing or voice, or will be autogenerated.
的分析查询将使用搜索、自然语言处理或语音生成，或者将自动生成。

Gartner predicts that by 2020, 50 percent of analytical queries will be generated via search, natural language processing(NLP), or voice.
Gartner预测，到2020年，50%的分析查询将通过搜索、自然语言处理(NLP)或语音产生。

Of analytical queries will be generated via search, natural-language processing or voice, or will be automatically generated.
到2020年，50%的分析查询将通过搜索、自然语言处理或语音生成，或自动生成。

Gartner also forecasts that as much as 50% of analytical queries will be generated automatically using voice technology and augmented by Natural Language Processing(NLP).
对话查询Gartner还预测，多达50％的分析查询将使用语音技术自动生成，并通过自然语言处理（NLP）进行增强。

By 2020, 50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated.
到2020年，50％的分析查询将通过搜索，自然语言处理（NLP）或语音生成，或者将自动生成。

For instance, basic queries will make their way into General Questions.
例如，基本查询将进入通用问题。

Whereas data engineers queries will be focused on cleaning up and transforming data.
但是数据工程师的查询会更关注于数据的清理和转换。

By 2020, 50% of analytic queries will be generated using search, natural-language processing or voice, or will be autogenerated.
到2020年，50%的分析问题将通过使用搜索、自然语言处理和语音的方式生成，或者将被自动生成。

By 2020, 50% of analytical queries will be generated via search, natural-language processing or voice, or will be automatically generated.
到2020年，50%的分析查询将通过搜索、自然语言处理或语音生成，或自动生成。

This query will result in the names list of all the users.
此查询将生成所有用户的名称列表。

This query will retrieve every STOP message received depending on the chosen time range.
此查询将根据所选的时间范围检索收到的每条STOP消息。

Each query will do the disk I/O on the stable storage, which can dominate application execution time.
每个查询会做稳定存储，它可以主宰应用程序执行的磁盘I/O时间。

It's very simple: the users query will return to us an array of one or more Users.
这很简单：用户查询将返回给我们一个或多个用户的数组。

Our query will change a little to accommodate a filter, which allows us to execute structured searches efficiently:.
我们的语句将做一些改变用来添加过滤器(filter),它允许我们有效的执行一个结构化搜索：.

Our query will change a little to accommodate a filter, which allows us to execute structured searches efficiently:.
我们的语句将添加过滤器(filter),它使得我们高效率的执行一个结构化搜索：.

Where the DepartmentID of these tables match(i.e. the join-predicate is satisfied), the query will combine the LastName, DepartmentID and DepartmentName columns from the two tables into a result row.
在两表DepartmentID匹配之处(如连接谓词被满足)，查询将组合两表的LastName，DepartmentID和DepartmentName等列，把它们放到结果表的一行(一条记录)里。

Soon, the tens of billions of internet queries made each day will require AI, which means that each query will require billions more math operations.
很快，每天数百亿个来自互联网的请求（queries）都会需要人工智能，也就意味着，每个请求将需要超过数十亿词数学运算。