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：
- 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“.
准则1 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”。
- 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.
准则2 透明性准则
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。
- 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.
准则3 存取能力推测
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。
- Consistent reporting performance
Performance of the OLAP tool should not suffer significantly as the number of dimensions is increased.
准则4 稳定的报表性能
随着维度数量的增加，OLAP工具的性能不会受到显著影响。
- 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.
准则5 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。
- Generic Dimensionality
Every data dimension should be equivalent in its structure and operational capabilities.
准则6 维的等同性准则
每个数据维度的结构和操作能力都应相同。
- Dynamic sparse matrix handling
The OLAP server’s physical structure should have optimal sparse matrix handling.
准则7 动态的稀疏矩阵处理准则
OLAP服务器的物理结构应具有最佳的稀疏矩阵处理。
- Multi-user support
OLAP tools must provide concurrent retrieval and update access, integrity and security.
准则8 多用户支持能力准则
OLAP工具必须提供并发检索和更新访问，完整性和安全性。
- 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.
准则9 非受限的跨维操作
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。
- 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.
准则10 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。
- Flexible reporting
Reporting facilities should present information in any way the user wants to view it.
准则11 灵活的报告生成
报告工具应以用户想要查看的任何方式显示信息。
- Unlimited Dimensions and aggregation levels.
准则12 不受限的维度和聚合层次