Intelligent presentation of the right visualization

In the previous blog, we saw how different characteristics of the data can guide the process of automatic selection of the best control. In order to support the manager with an attractive and effective interface of the information stored in the database, intelligent graphics presentation might be employed. The process of intelligent graphics presentation is directed by five components as is depicted in the figure below.

Mapping data objects to interaction objects

Data is the basic ingredient of information. In the previous blogs, I outlined those parts of the steering model that are subject to variation. Those information-shaped elements are extracted from various internal and external data sources. The internal structure (meta data model) of the data source can give a large amount of extra information that is useful in designing effective interfaces in particular in rendering graphs.

Relationships within hierarchies: important rule

Often the attributes linked to the dimensions in a hierarchy are closely related to each other and are often attributes of the same entity. A customer dimension is related to the region dimension that in turn is related to the country dimension and this can be a reason to make a hierarchy in order to reduce the amount of categories of the dimensions. Dimensions of higher order (those dimensions that have the lowest amount of categories) should reside higher in the hierarchy. The product-line dimension is of a higher order then the product dimension and therefore the hierarchy is as follows: Product-line à Product.

Categories of a dimension

At run time, a dimension is filled with categories. The number of categories can be an indication to:

• Present a different type of graph;
• Decide to use replacement instead of insertion of the categories of the level below;
• Split the categories automatically into smaller groups.

If the number of categories exceeds the limit of 6, a pie chart would be inappropriate (Zelazny, 1996) or the rest of the categories should taken together in a single group ‘Other...’. Moreover, pie charts cannot be used if among the categories there are positive as well as negative numbers. In that case, a bar chart is a better choice.

Dimensions of a measure

Dimensions are points of view from where a user can look at the measures stored in the cube. They form the structure of the multidimensional model. A measure without dimensions does not make sense. This can be compared with a record with numbers having no corresponding primary key attributes or secondary explanatory data attributes. It is important, as indicated by the title of this blog, to allocate measures to a dimension, because not all measures do hold sensible values for a dimension.

Variations of measures

Now we have discussed the upper region of the steering model I will take a look at the lower region of the model. The physical storage for the measures and dimensions are often called cubes. Cubes are three-dimensional. Many people are familiar with two dimensional spreadsheet software packages, which establish a co-ordinate system by using rows and columns.