PivotCube VCL key features

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. End-user can easy build(define) own hierarchies for any dimension at runtime, save these hirarchies into slice file and restore them when it needed. In that manner he can define various hierarchies for same dimension. For examle, dimension Ware can be structured by Ware-type or Ware-manufacturer etc.

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:

1. Min
2. Max
3. Median
4. 1stQuartile
5. 3rdQuartile
6. InterQuartile
7. Quartile Deviation
8. Coeff of. Quartile Deviation
9. Skewness
10. Kurtosis
11. Standard Deviation
12. Variance
13. Coeff. of Deviation
14. Mean St. Error
15. Mean Abs. Deviation
16. Custom measure calculation over full sorted data array.

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:

1. Summa
2. Count
3. Average

Simple load data from any TDataSet descendant.
Using TDataSet successors as datasource allows you easy load Data from:

1. Borland database engine (BDE)
2. ActiveX data objects (ADO)
3. Direct Oracle Access (DOA)
4. IBObjects
5. 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.

Large number of dimensions and measures. Now PivotCube supports working with up to 255 dimensions and 1024 measures.

Saveand load built cubes into file or stream (via IStream interface) for any use in future including publications.

Save and load built cubes into XML storage for any use in future including publications in Web.

Save and load current cube slice into XML storage for any use in future including publications in Web.

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.

Simple sorting (with changing order) over any measure and dimension in direct and back order

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.

Filtering by measures is an important thing for detailed analysis. PivotCube provides a unique elaboration that allows excluding all data that don’t correspond conditions of filtering out of aggregation. For example, you want to exclude all sales less 10$ or to take into account only those customers who bought only one piece of ware etc.

Export PivotGrid into various media including HTML,Excel,TStrinGrid,Windows metafile or Printer via own printing engine

Calculated measures in PivotMap at runtime available via built-in formula language

Unicode characters support (require TNT unicode controls and PivotCube VCL (VCL sources) package