An OLAP query often needs read-only access of data records for summarization and aggregation.
通常，OLAP查询只需要对数据记录进行只读访问，以进行汇总和聚集。
Therefore, improving the OLAP query performance becomes the key issue in the field of data warehouse.
因此，提高联机分析处理的查询性能就成为了数据仓库领域的关键问题。
JOLAP's query model deals with OLAP query management, and its source model facilitates the implementation of vendor-specific primitive OLAP extensions.
JOLAP的查询模型处理OLAP查询管理，它的源模型方便了特定于供应商的OLAP原语扩展的实现。
JOLAP's query requirements and model facilitate the creation and execution of the three common types of OLAP query, namely: dimension queries, edge queries, and data queries.
JOLAP的查询需求和模型简化三种常见olap查询的创建和执行，即多维查询、边界查询和数据查询。
The techniques of the Data Warehousing includes the system structure of the data organization, the data collecting, data extraction, data loading, data cube and OLAP query technique.
该数据仓库建立技术包括数据组织体系结构、数据采集、抽取、加载、数据立方体及OLAP功能查询技术。
The CWM makes a clear distinction between client-side metadata, which a client requires to query OLAP resources, and server-side metadata, which an OLAP server needs to process a client request.
CWM清楚地区分客户端元数据与服务器端元数据，客户机需要前者来查询OLAP资源，而 OLAP服务器需要后者来处理客户机请求。
OLAP servers and applications are commonplace and many storage schemes, query mechanisms, and access strategies have been developed to meet the business demand for complex analytical querying.
OLAP服务器和应用程序随处可见，并且很多存储模式、查询机制和访问策略也已经被开发出来，以满足复杂分析查询的业务需求。
This is the simplest form of an OLAP style query.
这是最简单的一种olap式查询。
The database may contain materialized query tables (MQTs), the pre-joined and pre-aggregated tables for DWE OLAP models and cubes.
数据库可以包含物化查询表（MQT），即用于 DWEOLAP模型和多维数据集的预联结和预聚合的表。
The transformation libraries provide query planning logic for all supported OLAP queries and also support all functionality that the query framework provides in the Compatible Query Mode.
转换库为所有受支持的OLAP查询提供查询规划逻辑，且在CompatibleQuery Mode 下支持查询框架提供的所有功能。
From DWE design Studio, you can design and deploy materialized query tables (MQTs) for OLAP models and cubes.
在DWE DesignStudio中，可以为olap模型和多维数据集设计和部署物化查询表(MQT)。
The paper introduces optimizing data sources to improve the performance of a OLAP system and the methods of optimizing database, aggregation and memorizing client record query.
通过优化数据源提高OLAP系统的性能，文章详细介绍了优化数据库、聚合及采用记录客户查询的方法。
XQuery offers a solution to assist OLAP by providing a query-based link between multiple data warehouses.
XQuery提供了一种解决方案，通过在多个数据仓库之间建立基于查询的连接来帮助OLAP。
The key difference between olap4j and query-model-centric APIs such as JSR-69 and Oracle's OLAP API is that in olap4j use of the query model is optional.
olap4j和以查询模型为中心的API（比如JSR-69和Oracle的OLAPAPI）相比，最关键的区别在于olap4j里查询模型是可选的。
When using OLAP or DMR-based packages, reports with expressions that compare values with different data types will produce the following error in Dynamic Query mode.
使用基于olap或dmr的包时，如果报告包含比较不同数据类型的值的表达式，那么以dynamicQueryMode执行报告将生成以下错误消息。
Data warehouses stores volumes of historical data, and OLAP applications involve complex queries on these data. Since response time should be small, query optimization is critical.
数据仓库存储大量历史数据，OLAP应用涉及到对大面积历史数据的复杂查询，查询优化是提高OLAP响应速度的关键。
OLAP(Online Analytical Processing) is used in processing of bulk of data to support sophisticated query and decision-making.
联机分析处理（OLAP）用于对企业大量数据进行分析，以支持复杂查询和决策。
Based on the customer-centric data warehouse, several methods for customer data analysis, such as query and report, OLAP and data mining, are introduced.
基于建立的客户数据仓库，介绍了查询与报表、OLAP分析和数据挖掘等客户数据分析方法。
The IT-intensive approach is one of managed reporting, end-user query, OLAP, dashboards, scorecards and data mining.
与IT紧密相关的办法是管理报告，终端用户查询，多维分析，仪表板，记分卡和数据挖掘的一种。
The front application systems use some data analytic methods, for example static report, dynamic query, OLAP (Online analytic Processing) and data mining techniques.
前台应用系统实现和应用了多种数据分析方法，包括：静态报表、动态查询、多维在线分析及数据挖掘等。
A smart OLAP cube will realize when you should use an aggregate and re-write your query to use the aggregated data instead.
一个聪明的OLAP多维数据集将实现时，你应该使用一个聚合和重写你的查询，而不是使用汇总数据。
Absrtact: the query processing of the OLAP of the data warehouse usually involves complex query on a large number of datum.
摘要：在数据仓库的联机分析处理的查询处理中，经常会涉及到大量数据的复杂即席查询。
BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
商务智能的应用包括支持性决策系统，提问及报告，在线分析程序，统计分析，预测及数据探测。
BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
商务智能的应用包括支持性决策系统，提问及报告，在线分析程序，统计分析，预测及数据探测。
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