OLAP的12條準則

• Multidimensional conceptual view OLAP模型必須提供多元概念視圖

  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".

  分析使用者能自然的視企業為一個多元模型，例如，利潤可以按區域，産品，時間，或方案（如實際，預算或預測）檢視。多元資料模型能讓使用者更直接和友善的操作資料，包括“切片和切塊”

• Transparency 透明性

  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.

  當OLAP以使用者習慣的方式提供電子表格或圖形顯示時，這對使用者應該是透明的。OLAP應該是開發系統架構的一部分，這個架構能按使用者的需要嵌入到任何地方，而不會對主機工具的功能産生副作用。使用者不應該接觸到提供給OLAP工具的資料源，這些資料可能是同構的或是異構的

• Accessibility 存取能力準則

  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.

  OLAP工具應該有能力利用自有的邏輯結構通路異構資料源，并且進行必要的轉換以提供給使用者一個連貫的展示。是OLAP工具而不是使用者需要關心實體資料的來源

• Consistent reporting performance 穩定的報表能力

  Performance of the OLAP tool should not suffer significantly as the number of dimensions is increased.

  OLAP工具的性能不應該因次元增加而受到明顯的影響

• Client/server architecture 客戶/伺服器體系結構

  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.

  OLAP工具的伺服器端應該足夠的智能讓多客戶的以最小的代價連接配接。伺服器應該有能力映射和鞏固不同資料庫的資料

• Generic dimensionality 維的等同性準則

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

  每個資料次元應該具有等同的結構和操作能力

• Dynamic sparse matrix handling 動态的稀疏矩陣處理

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

  OLAP伺服器的實體結構應能處理最優稀疏矩陣

• Multi-user support  多使用者支援能力

  OLAP tools must provide concurrent retrieval and update access, integrity and security.

  OLAP應提供并發擷取和更新通路，保證完整和安全的能力

• Unrestricted cross-dimensional operations 非受限的跨維操作

  Computational facilities must allow calculation and data manipulation across any number of data dimensions, and must not restrict any relationship between data cells.

  計算裝置必需允許跨資料次元的計算和資料操作，不能限制任何資料單元間的關系

• Intuitive data manipulation 直覺的資料操縱

  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.

  資料操作應在固定的路徑下，例如鑽或縮小，應該通過直接在分析模型的單元上完成，而不需要目錄貨多次的使用者互動

• Flexible reporting 靈活的報表生成

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

  報表裝置應該能以使用者需要的任何方式展現資訊

Unlimited dimensions and aggregation levels. 不受限的維與聚集層次 

The number of data dimensions supported should, to all intents and purposes, be unlimited. Each generic dimensions should enable an essentially unlimited number of user-defined aggregation levels within any given consolidation path.

資料次元數量應該是無限的，使用者在每個通用次元上定義的聚集聚合層次應該是無限的。

譯自：http://www.olap.com/w/index.php/Codd’s_Paper

深入閱讀：http://www.bi-verdict.com/fileadmin/FreeAnalyses/fasmi.htm

轉自：http://bookcold.com/2010/05/479