A cross tab displays summary, or aggregate values, such as sums, counts, or averages, in a row‑and‑column matrix similar to a spreadsheet. For example, you can use a cross tab to view sales data for each product line, by year, or total sales for each product line, by geography, and so on. Actuate enables you to use BIRT Designer Professional, BIRT Studio, or Interactive Crosstabs to create a cross tab. This document discusses how you can create a cross tab using Interactive Crosstabs and modify it using Interactive Viewer and Interactive Crosstabs.

The aggregate values in a cross tab are arranged in dimensions and measures, and each cross tab can display multiple dimensions and multiple measures. The data for a cross tab is derived from a cube or a data model, which also arrange data in dimensions and measures. Dimensions are categories, such as products, customers, or sales periods, by which measures are aggregated. Measures represent values that are counted or aggregated, such as costs or units of products. Each detail cell in a cross tab displays an aggregate value.

A cross tab consists of the following three areas, as shown in Figure 9‑1:

The row and column areas contain the dimension values. The detail area contains one or more measures displaying aggregate data.

Figure 9‑1 shows the example report, which groups data by year. Each number in the cross tab represents the sales total of a country for a particular year. Grand Total displays the total sales across years for each country, and the total sales for each year across all countries. Grand Total also displays the total sales across all countries for all years.

In BIRT iHub and later releases, a cross tab obtains data from a BIRT data object containing a cube or data model. A cube is a multidimensional data structure optimized for analysis. A data model consists of several data sets in a data object linked by joins. Both cubes and data models organize data in dimensions, measures, and attributes. Dimensions can contain multilevel hierarchies, enabling users to drill down to view detailed information or drill up to view summary information for each level within the hierarchy. For example, a time dimension can provide the ability to display data by day, week, month, quarter, or year. The time dimension also supports storing data in the time periods a data object developer specifies. A geographical dimension can provide the ability to display data by region, country, state, or city, enabling the user to view high level summary information or details for each dimension level.

Query optimization in a data model offers better performance than a cube. A data model also provides you with more flexibility in your choice of dimensions and measures.