A methodological framework for data warehouse design
Proceedings of the 1st ACM international workshop on Data warehousing and OLAP
Data modelling versus ontology engineering
ACM SIGMOD Record
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Reconciling requirement-driven data warehouses with data sources via multidimensional normal forms
Data & Knowledge Engineering
Automating multidimensional design from ontologies
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
GRAnD: A goal-oriented approach to requirement analysis in data warehouses
Decision Support Systems
Data modeling techniques for data warehousing
Data modeling techniques for data warehousing
Model-independent schema translation
The VLDB Journal — The International Journal on Very Large Data Bases
The Data Warehouse Lifecycle Toolkit
The Data Warehouse Lifecycle Toolkit
Logic programming for data warehouse conceptual schema validation
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
A model-driven heuristic approach for detecting multidimensional facts in relational data sources
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Multidimensional design by examples
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Metadata for approximate query answering systems
Advances in Software Engineering
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The main methodologies for the data warehouse design are based on two approaches which are opposite and alternative each other. The one, based on the data-driven approach, aims to produce a conceptual schema mainly through a reengineering process of the data sources, while minimizing the involvement of end users. The other is based on the requirement-driven approach and aims to produce a conceptual schema only on the basis of requirements expressed by end users. As each of these approaches has valuable advantages, it is emerged the necessity to adopt a hybrid methodology which combines the best features of the two approaches. We introduce a conceptual model that is based on a graph-oriented representation of the data sources. The core of the proposed hybrid methodology is constituted by an automatic process of reengineering of data sources that produces the conceptual schema using a set of requirement-derived constraints.