Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Scalable semantic web data management using vertical partitioning
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
SOR: a practical system for ontology storage, reasoning and search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
SPARQL basic graph pattern optimization using selectivity estimation
Proceedings of the 17th international conference on World Wide Web
Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in Oracle
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
OntoDB: an ontology-based database for data intensive applications
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Context representation in domain ontologies and its use for semantic integration of data
Journal on data semantics X
View selection in Semantic Web databases
Proceedings of the VLDB Endowment
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The spectacular use of ontologies generates a big amount of semantic instances. To facilitate their management, a new type of databases, called semantic databases ($\mathcal{S}\mathcal{D}\mathcal{B}$) is launched. Large panoply of these $\mathcal{S}\mathcal{D}\mathcal{B}$ exists. Three main characteristics may be used to differentiate them: (i) the storage layouts for storing instances and the ontology, (ii) ontology modeling languages, and (iii) the architecture of the target database management system (DBMS) supporting them. During the deployment phase, the database administrator (DBA) is faced to a choice problem (which $\mathcal{S}\mathcal{D}\mathcal{B}$ she/he needs to choose). In this paper, we first present in details the causes of this diversity. Based on this analysis, a generic formalization of $\mathcal{S}\mathcal{D}\mathcal{B}$ is given. To facilitate the task of the DBA, mathematical cost models are presented to evaluate the performance of each type of $\mathcal{S}\mathcal{D}\mathcal{B}$. Finally, two types of intensive experiments are conducted by considering six $\mathcal{S}\mathcal{D}\mathcal{B}$, both issued from industry and academic communities; one based on our mathematical cost models and another based on the studied semantic DBMS cost models.