Constructing OLAP cubes based on queries
Proceedings of the 4th ACM international workshop on Data warehousing and OLAP
A foundation for capturing and querying complex multidimensional data
Information Systems - Data warehousing
Modeling Multidimensional Databases, Cubes and Cube Operations
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Multidimensional normal forms for data warehouse design
Information Systems
Research in data warehouse modeling and design: dead or alive?
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Hierarchies in a multidimensional model: from conceptual modeling to logical representation
Data & Knowledge Engineering - Special issue: WIDM 2004
Automating multidimensional design from ontologies
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
Integrating Data Warehouses with Web Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Ontologies and summarizability in OLAP
Proceedings of the 2010 ACM Symposium on Applied Computing
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
Transforming statistical linked data for use in OLAP systems
Proceedings of the 7th International Conference on Semantic Systems
Multidimensional integrated ontologies: a framework for designing semantic data warehouses
Journal on Data Semantics XIII
An ETL process for OLAP using RDF/OWL ontologies
Journal on Data Semantics XIII
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The amount of Linked Data containing statistics is increasing; and so is the need for concepts of analysing these statistics. Yet, there are challenges, e.g., discovering datasets, integrating data of different granularities, or selecting mathematical functions. To automatically, flexibly, and scalable integrate statistical Linked Data for expressive and reliable analysis, we propose to use expressive Semantic Web ontologies to build and evolve a well-interlinked conceptual model of statistical data for Online Analytical Processing.