OLAP的12條規則
Edgar F. Codd於1985年撰寫了一篇論文，定義了關係資料庫管理系統（RDBMS）的規則，這些規則徹底改變了IT行業。 1993年，Codd及其同事研究了以下 12 條規則，用於定義OLAP（線上分析處理）。這是一個可以在多維空間中整合和分析資料的行業。
Codd的12條規則是：
- 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“.
多維概念檢視 在使用者分析師看來，企業天然是多維的。 例如，可以按地區，產品，時間段或方案（例如實際，預算或預測）檢視利潤。多維資料模型使使用者能夠更直接，更直觀地處理資料，包括“分片和分塊”。
- 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 當OLAP構成使用者習慣電子表格或圖形包的一部分時，這應該對使用者透明。 OLAP應該是開放系統體系結構的一部分，該體系結構可以嵌入到使用者期望的任何位置，而不會不利地影響宿主工具的功能。使用者不應暴露於提供給OLAP工具的資料來源，這可能是同構的或異構的。
- 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.
無障礙 OLAP工具應該能夠應用自己的邏輯結構來訪問異構資料來源，並執行向用戶呈現連貫檢視所需的任何轉換。工具（而不是使用者）應關注物理資料的來源。
- Consistent reporting performance Performance of the OLAP tool should not suffer significantly as the number of dimensions is increased.
一致的報表效能 隨著維度數量的增加，OLAP工具的效能不會受到顯著影響。
- 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.
客戶/伺服器架構 OLAP工具的伺服器元件應該足夠智慧，各種客戶端可以輕鬆地連線它。伺服器應該能夠在不同的資料庫之間對映和合並資料。
- Generic Dimensionality Every data dimension should be equivalent in its structure and operational capabilities.
通用維度 每個資料維度的結構和操作能力都應相同。
- Dynamic sparse matrix handling The OLAP server’s physical structure should have optimal sparse matrix handling.
動態稀疏矩陣處理 OLAP伺服器的物理結構應具有最佳的稀疏矩陣處理。
- Multi-user support OLAP tools must provide concurrent retrieval and update access, integrity and security.
多使用者支援 OLAP工具必須提供併發檢索和更新訪問，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.
不受限制的跨維操作 計算設施必須允許跨任意數量的資料維度進行計算和資料處理，並且不得限制資料單元之間的任何關係。
- 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.
直觀的資料操作 合併路徑中固有的資料操作，例如向下鑽取或縮小，應通過對分析模型單元的直接操作來完成，而不需要使用選單或跨使用者介面多次行程。
- Flexible reporting Reporting facilities should present information in any way the user wants to view it.
靈活的報告 報告工具應以使用者想要檢視的任何方式顯示資訊。
- Unlimited Dimensions and aggregation levels.
無限維度和聚合級別。
翻譯自：ofollow,noindex" target="_blank">http://olap.com/learn-bi-olap/codds-paper/