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Moreover, ATL offers two kinds of constructors, namely imperative and declarative, for models construction. In order to illustrate the steps of our DM construction method by transformations according to MDA, we give in figure 1 the Meta model of the relational DW.
This diagram is produced using EMF. Meta model of the relational DW Fig. Star schema multidimensional concepts 3. Each column has a basic data type and may participate to the definition of a primary key. A column may reference another column to establish a navigational relationship between rows belonging to the same table or to different tables Foreign key concept.
The fact is the business process of interest for decision making; it is the subject of OLAP analyses  . Conceptually speaking, a fact is composed of a finite set of attributes called measures. Measures represent indicators reflecting the business activity to be analyzed.
In multidimensional modeling, a fact is an n-ary relationship between dimensions.
Dimensions represent simultaneously the axes according to which the fact measures are recorded and then analyzed. Each dimension is made up of a set of attributes. Some attributes of a dimension are often semantically ordered from the finest to the highest granularity, these ordered attributes are said parameters; they define the concept of hierarchy in multidimensional modeling.
In fact, within hierarchies, parameters represent levels for aggregating measures. Feki Fig. Multidimensional Meta model for data marts Figure 2 is a star schema; it illustrates the multidimensional concepts. Figure 3 is a class diagram describing the multidimensional Meta-model of DMs. It is the target Meta model of transformations.
They produce DM schemas starting from a relational PIM model following two consecutive tasks: i Identification of multidimensional components from within the relational DW data model, and ii multidimensional schema construction in accordance with the constraints of the target data model.
The application of these rules generates a target multidimensional PIM. Fact rule. A source table transforms into a fact if it contains at least one non-key numeric attribute. We are not interested with empty facts as they are rare in practice. Measure rule. Within each source table identified as a fact, we look for non-key numerical attributes and transform them into measures, if any.
Dimension rule. Within each table T identified as a fact F, we find all its foreign keys and then, we transform each table referenced by a foreign key into a dimension for F. In addition, each numeric column belonging to the primary key of table T transforms into a dimension for fact F.
Each table transformed into a dimension is likely to provide parameters which are its non-key columns. Since these parameters are extracted from the same table, we consider them at the same hierarchical level. This step will be reiterated until all possible navigation paths are followed.
More precisely, we operate according to the two following steps: 1. Create the first parameter. The finest parameter i. We define a recurrent rule; it is to navigate between tables linked via foreign keys where we extract parameters.
Iteration i produces the ith parameter. Weak attributes rule.
This rule associates to each parameter, issued from table T, all non-key columns of T. DATE dimension rule.
The dynamic of the organization activities is traced through its business processes that generate transactional data over time. It is generally built on a Date attribute via trivial transformations.
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Le terme anglais de business intelligence BI peut porter confusion avec Ross, W. Thornthwaite, Le Data Warehouse: Guide de conduite de projet, Eyrolles, Le projet Data Warehouse, un processus continu. En fait, le projet Data Warehouse est un processus.