The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
A relational model of data for large shared data banks
Communications of the ACM
A foundation for capturing and querying complex multidimensional data
Information Systems - Data warehousing
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Summarizability in OLAP and Statistical Data Bases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Extending the E/R Model for the Multidimensional Paradigm
ER '98 Proceedings of the Workshops on Data Warehousing and Data Mining: Advances in Database Technologies
Gradual data aggregation in multi-granular fact tables on resource-constrained systems
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Using a time granularity table for gradual granular data aggregation
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Original paper: Data modeling to facilitate internal traceability at a grain elevator
Computers and Electronics in Agriculture
Definition and analysis of new agricultural farm energetic indicators using spatial OLAP
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II
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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.