• These capabilities include sorting, grouping, aggregating, and a large set of functions to manipulate the different data types.

    这些功能包括排序分组聚集以及一个操作不同数据类型大型函数

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  • Smart large objects consist of character large object (CLOB) and binary large object (BLOB) data types.

    智能对象包括字符对象(CLOB)二进制大对象(BLOB)数据类型

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  • Relational databases that store XML data usually use Binary Large Object (BLOB) field types to store XML.

    存储XML数据关系数据库通常使用大型二进制对象(BLOB)字段类型存储XML。

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  • Text indexes can be created on a range of data types from small VARCHARs to large BLOBs that contain text objects.

    文本索引可以广泛范围数据类型创建,包括小型的VARCHAR和大型BLOB包含文本对象的数据类型。

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  • Support for XML and LOB (large object) data types of any size.

    支持xmllob(对象)数据类型

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  • The maximum length for a VARCHAR column may seem sufficiently large, but Derby provides larger character data types, which will be discussed in a future article.

    VARCHAR最大长度看起来可能足够了,但是Derby 还提供了大的字符数据类型这个类型未来文章中讨论

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  • Smart LOBs consist of character large objects (CLOBs) and binary large objects (BLOBs) data types.

    智能对象字符对象(CLOBs)二进制大对象(BLOBs)数据类型组成。

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  • Despite their large size tables are actually pretty restrictive, for they only support a handful of data types are require developers to think about partitioning from the very beginning.

    尽管Table Service可以支持容量数据,但由于支持少量的数据类型,所以实际上还是有比较严格的限制,要求开发人员开始需要考虑如何处理分区问题。

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  • Data types for realistic business applications are often both large and complex, having many properties and many levels of nesting.

    实际业务应用程序数据类型通常较大而且内容复杂并具有许多属性多层嵌

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  • The HAT tool produces statistics for data types that have large number of instances in a single dump and can also compare two heap dumps to identify data types which have increased in number.

    HAT工具单个转储具有大量实例数据类型生成统计信息并且可以比较两个转储标识数量增加的数据类型。

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  • The comparative analysis technique identifies a set of large sized data structures experiencing significant growth in the number of instances of constituent data types.

    比较分析技术确定大规模数据结构,它们许多组成数据类型实例数目方面快速增长

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  • What data types and data structures are causing a large footprint?

    什么数据类型数据结构导致占用大量的空间?

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  • Lists suspected data structures, data types, and packages contributing to the growth in heap usage for comparative analysis, and to the large size of the heap for single dump analysis.

    列出导致使用增长可疑数据结构、数据类型数据包,用于比较分析;列出大规模堆,用于单一传储分析。

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  • Using very large or very small Numbers or emulating private data-types derived from numeric types.

    使用极大极小数字或者模仿数值类型派生的私有数据类型。

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  • If I have to do bit manipulation of large amounts of binary data, why is it, that Scala's primitive types are immutable?

    如果需要大量二进制数据上进行字节操作,这就问题了,Scala的基本类型是常量化的吗?

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  • Besides reorganizing one or more indexes, large object data types (LOBs) that are contained in the clustered index or underlying table are compacted by default when an index is reorganized.

    重新组织索引时,除了重新组织一个多个索引外,默认情况下将压缩聚集索引或基础包含大型对象数据类型(lob)。

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  • Many different types sensors were fixed in each important spots of a large-scale, complex structure, in order to acquire the data reflecting the health state of the structure.

    结构健康监测中,对于大型、复杂的结构,获得反映整个结构健康状态数据参数,就必须结构的各个重要部位安装多个不同类型传感器

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  • The main achievements are as follows: 1 Display large remote sensing images of different data types and bands quickly.

    快速显示具有不同波段,不同数据类型数据量的遥感图像

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  • A large number data of ground hailstone spectra are analysed. The average characteristics of hailstone spectra and their three types have been obtained.

    文中分析了大量地面资料给出了雹谱的平均特征及其三种类型

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  • Large database contains data about more than 0.01 million types of automobiles, the data about new automobiles can be added periodically or aperiodically depending on new automobile login function.

    强大数据库内存一万多种车型数据新车登录功能定期不定期的增加新车数据。

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  • The common problem of it is that the surveillance area is generally large, hence it is difficult to arrive at an useful data fusion architecture due to timing, accuracy and different types of sensors.

    由于海上侦察目标区域过大且时间精度不同类型传感器原因,使得设计的数据融合系统实用性不强。

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  • The common problem of it is that the surveillance area is generally large, hence it is difficult to arrive at an useful data fusion architecture due to timing, accuracy and different types of sensors.

    由于海上侦察目标区域过大且时间精度不同类型传感器原因,使得设计的数据融合系统实用性不强。

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