Building the data warehouse (2nd ed.)
Building the data warehouse (2nd ed.)
Dimension Hierarchies Design from UML Generalizations and Aggregations
ER '01 Proceedings of the 20th International Conference on Conceptual Modeling: Conceptual Modeling
Modelling strategic relationships for process reengineering
Modelling strategic relationships for process reengineering
An analysis of additivity in OLAP systems
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
A UML profile for multidimensional modeling in data warehouses
Data & Knowledge Engineering - Special issue: ER 2003
A taxonomy of inaccurate summaries and their management in OLAP systems
ER'05 Proceedings of the 24th international conference on Conceptual Modeling
Inferring aggregation hierarchies for integration of data marts
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Improving the development of data warehouses by enriching dimension hierarchies with WordNet
ODBIS'05/06 Proceedings of the First and Second VLDB conference on Ontologies-based databases and information systems
Ontology-Driven conceptual design of ETL processes using graph transformations
Journal on Data Semantics XIII
Searching semantic data warehouses: models, issues, architectures
Proceedings of the 2nd International Workshop on Semantic Search over the Web
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Data warehouse dimension hierarchies are of paramount importance in OLAP (On-Line Analytical Processing) tools to support the decision-making process, since they allow the analysis of data at different levels of detail (i.e. levels of aggregation). This is why it is crucial to capture adequate hierarchies in the requirement analysis stage. However, operational sources may not be able to supply enough data to construct every level of these hierarchies. In this paper, we propose the application of semantic relations among WordNet concepts to enrich hierarchies by adding the required levels of aggregation. Decision makers will thus be able to achieve their information needs for analysis.