What You Always Wanted to Know About Datalog (And Never Dared to Ask)
IEEE Transactions on Knowledge and Data Engineering
Summarizability in OLAP and Statistical Data Bases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
Bridging the gap between OWL and relational databases
Proceedings of the 16th international conference on World Wide Web
Unifying data and domain knowledge using virtual views
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A survey on summarizability issues in multidimensional modeling
Data & Knowledge Engineering
Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses
Information Sciences: an International Journal
A framework for multidimensional design of data warehouses from ontologies
Data & Knowledge Engineering
A methodology and tool for conceptual designing a data warehouse from ontology-based sources
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
The MASTRO system for ontology-based data access
Semantic Web
ER'11 Proceedings of the 30th international conference on Advances in conceptual modeling: recent developments and new directions
Multidimensional integrated ontologies: a framework for designing semantic data warehouses
Journal on Data Semantics XIII
Proceedings of the 21st ACM international conference on Information and knowledge management
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Understandability, reuse, and maintainability of analytical queries belong to the key challenges of Data Warehousing, especially in settings where a large number of business analysts work together and need to share knowledge. To tackle these challenges we propose Ontology-based OLAP where an ontology acts as superimposed conceptual layer between business analysts and multidimensional data. In Ontology-based OLAP, dimensions and facts are enriched by concept definitions capturing the semantics of relevant business terms used to define measures and to formulate analytical queries. Using traditional ontology languages, it is, however, very difficult to capture the hierarchical and multidimensional conceptualizations of business analysts. In this paper, we propose hierarchical and multidimensional ontologies to better capture these structural specificities. We define and implement the abstract structure and semantics of multidimensional ontologies as rules and constraints in Datalog with negation and represent multidimensional ontologies as Datalog facts. In addition to reasoning over multidimensional ontologies (open-world) we discuss their grounding in Data Warehouses (closed-world) as the fundament of Ontology-based OLAP.