Designing data marts for data warehouses
ACM Transactions on Software Engineering and Methodology (TOSEM)
The Challenge of Process Data Warehousing
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Towards a Corporate Performance Measurement System
Proceedings of the 2004 ACM symposium on Applied computing
Goal-oriented requirement analysis for data warehouse design
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
A UML 2 profile for business process modelling
ER'05 Proceedings of the 24th international conference on Perspectives in Conceptual Modeling
Extending the REA Ontology for the Evaluation of Process Warehousing Approaches
WSKS '09 Proceedings of the 2nd World Summit on the Knowledge Society: Visioning and Engineering the Knowledge Society. A Web Science Perspective
An evaluation of process warehousing approaches for business process analysis
Proceedings of the International Workshop on Enterprises & Organizational Modeling and Simulation
RAMEPs: a goal-ontology approach to analyse the requirements for data warehouse systems
WSEAS Transactions on Information Science and Applications
Economic information systems for small and medium businesses and evaluation of return
WSEAS Transactions on Information Science and Applications
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In this paper we propose an approach to business process measurement and control. The process measurement is accomplished by a data warehouse. We propose a method for development of a conceptual model of a data warehouse based on GQ(I)M method to define business goals. Together with definition of business and measurement goals, entities and their attributes are identified. The model composed of these entities is designed with UML 2.0 structure diagram in accordance with our proposed indicator determination metamodel. The indicators are identified in compliance with GQ(I)M method and defined with OCL expressions using entities and attributes of the model. Then, based on the structure of the OCL expressions, potential facts and dimensions of the data warehouse are identified. The implementation of the proposed solution is discussed for the university data warehouse project.