PivotCube VCL的主要特點：

樹狀（層次）維度
它不但可以處理線性維度，還可以處理層次維度。我們把它們稱為樹狀，是因為它們的構造就像標準的層次結構—樹一樣，對于樹，所有Windows用戶應該都很熟悉（目錄的結構就像樹）。因此，你可以很容易地建立結構類似于Windows目錄的維度樹，它可以具有無數的葉子和節(jié)點。葉子和節(jié)點可以層層嵌套，最多可以有255層。
　
可擴展的統計功能
要使用這個功能，你需要建立過度飽和立方體。但在此之前，你需要把PivotCube.ExtendedMode這個屬性設為True。如果一個立方體單元容納了很多事實表中的數據，那么統計計算就會變的很復雜。盡管如此，PivotCube仍然提供了基于沒有經過簡化處理的完整數據集的計算。這個特點使你可以計算關于當前過濾集的準確函數值。這個特點的獨特之處在于精確的計算而不是基于簡化的統計公式的計算。因此，使用PivotCube，你總是可以得到準確的計算結果。

PivotCube擴展模式支持的統計功能如下：

• 最小值
• 最大值
• 中間值
• 四分之一
• 四分之三
• 四分點之和
• 四分點之差
• 四分點偏差系數
• 偏斜度
• 峰度
• 均方差
• 方差
• 偏差系數
• 平均誤差
• 平均絕對值差

    如果你不需要這個特殊功能，你可以使用PivotCube的標準模式來最小化立方體大小，內存占用和提高速度。但在此之前，你需要把PivotCube.ExtendedMode屬性設為False。

PivotCube標準模式下的功能：

• 總和
• 計數
• 平均數

    可以很簡單地從任何TDataSet的派生類裝載數據
    使用TDataSet的派生類作為數據源使你可以輕松地通過以下方式裝載數據：

• Borland數據庫引擎（BDE）
• ActiveX數據對象（ADO）
• Direct Oracle Access(DOA)
• IBObjects

    雖然你沒有必要使用“Group by”或者MDX語句對數據進行預處理，但是如果你希望從SQL-Server裝入PivotCube的數據記錄數最少，你就可以使用“Group by”語句

• 可以很容易地使用新數據更新已有的立方體（不需要重新構造整個立方體）
• 就像在一個大型的OLAP服務器中，通過在已有的立方體中添加新的數據，而不是重新構建整個立方體，你可以一步一步地構造自己的立方體。對于那些需要處理經常變化的數據的用戶來說，這是非常重要的特色。

自定義的維包裝（dimension wrapping）

　　例如，“日期”可以被切分為季節(jié)、季度、白天/夜晚等等，或者，“地址”可以被切分為街道、郵政編碼、城市、村等。“姓” [比如smith]，“名” [比如John]和“部門”[比如managers]可以被綜合成單個字符串“Employer”[比如 John Smith Mgrs.]
　

可通過維度和度量值篩選

    OLAP最強大的功能之一是能幫助用戶進行深刻和詳細地分析，從而得出正確的商業(yè)決策。PivotCube提供了強大的通過維度和度量值進行篩選的功能。

• 通過維度進行篩選以下面兩種方式進行:
  
• 常規(guī)篩選（通常過濾掉不必要的數據）
• 增量式的篩選（只篩選出所選擇的一個必要的維度元素；但仍然可以分析無數的維度）。當你只需要提供一個客戶的分析或者是一年的分析時，這種方式是非常方便的。
• 通過維度進行篩選：對于活動和不活動的維來說，都是可行的。例如，你不需要將一個維嵌入到一個活動的片段中進行分析。

PivotCube VCL key features

Tree-like (Hierarchical) dimensions

This feature allows working with not only linear dimensions, but with hierarchical dimensions as well. We call them “ Tree-like” because they are built like standard hierarchical structures – trees, good known to all Windows users (structure of directories is built like trees). So you can easily build dimensions which structure is branched like Windows directories and the quantity of nodes and leafs of the tree is unlimited. Both nodes and leafs can be embedded into nodes without any limitations up to 255 nested levels.

Extended set of statistical functions support.

To use this feature you need to build supersaturated cube. But before start building you need to set PivotCube.ExtendedMode to True. If one cube cell holds many values from the fact table - calculations of statistical functions get complicated. But nevertheless PivotCube provides calculations based on full data set without simplifying transformation of data. This allows calculating True function values regarding current filter sets. Unique thing of this feature is exact PivotCube calculations in contrary to calculations based on simplifying statistical formulas. So with PivotCube you always get True calculations as a result.

Statistical functions supported by PivotCube Extended Mode are as following:

• Min
• Max
• Median
• 1stQuartile
• 3rdQuartile
• InterQuartile
• Quartile Deviation
• Coeff of. Quartile Deviation
• Skewness
• Kurtosis
• Standard Deviation
• Variance
• Coeff. of Deviation
• Mean St. Error
• Mean Abs. Deviation

If you don’t need this special function you are able to minimize cube size, memory occupation and increase building speed using PivotCube Standard Mode. But before start building you need to set PivotCube.ExtendedMode to False.

List of aggregation functions allowed by PivotCube in Standard Mode:

• Summa
• Count
• Average

Simple load data from any TDataSet descendant.

Using TDataSet successors as datasource allows you easy load Data from:

• Borland database engine (BDE)
• ActiveX data objects (ADO)
• Direct Oracle Access (DOA)
• IBObjects
• Etc…

You don’t need to prepare you data with “Group by” or MDX clauses, but you may use “Group by” only if you wish to minimize loading data records into PivotCube from your SQL-server.
Easily upgrade saved cube with new data (without rebuilding total cube).
You can build your own cubes like in a 'large' OLAP servers – step-by-step, without rebuilding total cube, just adding new records into saved cube. It’s a very important feature especially for those users who work with often changing data.

Custom dimension wrapping

For example “Date” field can be splitted to Seasons, Quarters, Day/Night etc, or “Address” field can be splitted to street, zip-codes, city, village etc, fields “LastName” [e.g. Smith] “FirstName” [e.g. John] and “Department” [e.g. managers] can be combined to single string field “Employer” [e.g. John Smith mgrs.]

Filtering by dimensions and measures

• One of the most powerful OLAP features that helps to execute deep and detailed analysis to make business decisions is filtering. PivotCube provides powerful filtering by dimensions and filtering by measures.
  Filtering by dimensions is presented in 2 ways:
  • Custom filtering (ordinary filtering with the excluding of unnecessary data)
  • Incremental filtering (filtering with choosing one necessary dimension element only; quantity of analyzing dimensions is still unlimited). This is very convenient if you want for instance to provide analysis by one customer only or/and for only one year etc.
• Filtering by dimensions is available both in active and inactive dimensions. I.e. you don’t need to imbed a dimension into active slice to drill.