Data modeling for Precision Dairy Farming within the competitive field of operational and analytical tasks
The interdisciplinary concept of Precision Dairy Farming sets very high standards for data management. Special consideration during its implementation must therefore be given to support both operational and analytical data uses (e.g., OLAP). The inclusion of both data views results in the data modeling being a hybrid of two conceptual design models. In contrast to previous design concepts, we will assume a parallel modeling process for both views, which results in a shared logical data schema. This is the only way to effectively avoid redundancies and inconsistencies on both the schema and data levels. Using an ongoing application as an example, we will explain both methods and results. In doing so, we will make use of the Entity-Relationship Model (E/RM) for modeling operational data. We will also make use of E/RM's multi-dimensional extension, the multi-dimensional Entity-Relationship Model (mE/RM), for modeling analytical data. In order to meet all application-specific modeling requirements, however, new representation elements must be introduced. Therefore, we propose both a property window to describe the subject of analysis, and also a marker for temporal restrictions to the values of analysis structures as an extension of the mE/RM. Starting from the two conceptual models, we will then describe the logical modeling in a shared relational schema. Both the transformation of conceptual notation elements into relational structures and the creation of a required meta model will be explained during this step. The procedures discussed in this paper are important for a variety of tasks in the field of Precision Dairy Farming and beyond.
data modeling precision dairy farming olap multi-dimensional entity-relationship model relational model data warehouse