OLAP的12条规则

Edgar F. Codd于1985年撰写了一篇论文，定义了关系数据库管理系统（RDBMS）的规则，这些规则彻底改变了IT行业。 1993年，Codd及其同事研究了以下 12 条规则，用于定义OLAP（在线分析处理）。这是一个可以在多维空间中整合和分析数据的行业。

Codd的12条规则是：

1. Multidimensional conceptual view User-analysts would view an enterprise as being multidimensional in nature – for example, profits could be viewed by region, product, time period, or scenario (such as actual, budget, or forecast). Multi-dimensional data models enable more straightforward and intuitive manipulation of data by users, including “slicing and dicing“.

多维概念视图 在用户分析师看来，企业天然是多维的。 例如，可以按地区，产品，时间段或方案（例如实际，预算或预测）查看利润。多维数据模型使用户能够更直接，更直观地处理数据，包括“分片和分块”。

1. Transparency When OLAP forms part of the users’ customary spreadsheet or graphics package, this should be transparent to the user. OLAP should be part of an open systems architecture which can be embedded in any place desired by the user without adversely affecting the functionality of the host tool. The user should not be exposed to the source of the data supplied to the OLAP tool, which may be homogeneous or heterogeneous.

透明度 当OLAP构成用户习惯电子表格或图形包的一部分时，这应该对用户透明。 OLAP应该是开放系统体系结构的一部分，该体系结构可以嵌入到用户期望的任何位置，而不会不利地影响宿主 工具 的功能。用户不应暴露于提供给OLAP工具的数据源，这可能是同构的或异构的。

1. Accessibility The OLAP tool should be capable of applying its own logical structure to access heterogeneous sources of data and perform any conversions necessary to present a coherent view to the user. The tool (and not the user) should be concerned with where the physical data comes from.

无障碍 OLAP工具应该能够应用自己的逻辑结构来访问异构数据源，并执行向用户呈现连贯视图所需的任何转换。工具（而不是用户）应关注物理数据的来源。

1. Consistent reporting performance Performance of the OLAP tool should not suffer significantly as the number of dimensions is increased.

一致的报表性能 随着维度数量的增加，OLAP工具的性能不会受到显著影响。

1. Client/server architecture The server component of OLAP tools should be sufficiently intelligent that the various clients can be attached with minimum effort. The server should be capable of mapping and consolidating data between disparate databases.

客户/服务器架构 OLAP工具的服务器组件应该足够智能，各种客户端可以轻松地连接它。服务器应该能够在不同的数据库之间映射和合并数据。

1. Generic Dimensionality Every data dimension should be equivalent in its structure and operational capabilities.

通用维度 每个数据维度的结构和操作能力都应相同。

1. Dynamic sparse matrix handling The OLAP server’s physical structure should have optimal sparse matrix handling.

动态稀疏矩阵处理 OLAP服务器的物理结构应具有最佳的稀疏矩阵处理。

1. Multi-user support OLAP tools must provide concurrent retrieval and update access, integrity and security.

多用户支持 OLAP工具必须提供并发检索和更新访问，完整性和安全性。

1. Unrestricted cross-dimensional operations Computational facilities must allow calculation and data manipulation across any number of data dimensions, and must not restrict any relationship between data cells.

不受限制的跨维操作 计算设施必须允许跨任意数量的数据维度进行计算和数据处理，并且不得限制数据单元之间的任何关系。

1. Intuitive data manipulation Data manipulation inherent in the consolidation path, such as drilling down or zooming out, should be accomplished via direct action on the analytical model’s cells, and not require use of a menu or multiple trips across the user interface.

直观的数据操作 合并路径中固有的数据操作，例如向下钻取或缩小，应通过对分析模型单元的直接操作来完成，而不需要使用菜单或跨用户界面多次行程。

1. Flexible reporting Reporting facilities should present information in any way the user wants to view it.

灵活的报告 报告工具应以用户想要查看的任何方式显示信息。

1. Unlimited Dimensions and aggregation levels.

无限维度和聚合级别。

翻译自： http://olap.com/learn-bi-olap/codds-paper/