Name
Abstract | ||
|---|---|---|
Extract-Transform-Load (ETL) is a crucial phase in Data Warehouse (DW) design life-cycle that copes with many issues: data provenance, data heterogeneity, process automation, data refreshment, execution time, etc. Ontologies and Semantic Web technologies have been largely used in the ETL phase. Ontologies are a buzzword used by many research communities such as: Databases, Artificial Intelligence (AI), Natural Language Processing (NLP), where each community has its type of ontologies: conceptual canonical ontologies (for databases), conceptual non-canonical ontologies (for AI), and linguistic ontologies (for NLP). In ETL approaches, these three types of ontologies are considered. However, these studies do not consider the types of the used ontologies which usually affect the quality of the managed data. We propose in this paper a semantic ETL approach which considers both canonical and non-canonical layers. To evaluate the effectiveness of our approach, experiments are conducted using Oracle semantic databases referencing LUBM benchmark ontology. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23201-0_14 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Ontology layers,ETL,Data quality,Consistency | Ontology (information science),Data warehouse,Ontology,Data mining,Confusion,Data quality,Information retrieval,Process automation system,Computer science,Semantic Web,Oracle | Conference |
Volume | ISSN | Citations |
539 | 1865-0929 | 1 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Selma Khouri | 1 | 117 | 15.53 |
| Sabrina Abdellaoui | 2 | 1 | 0.34 |
| Fahima Nader | 3 | 4 | 3.75 |