Data Warehouses and the Semantic Web
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The availability of enormous amounts of data from many different domains is producing a shift in the way data warehousing practices are being carried out. Massive-scale data sources are becoming common, posing new challenges to data warehouse practitioners and researchers. The semantic web, where large amounts of data are being stored daily, is a promising scenario for data analysis in a near future. As large repositories of semantically annotated data become available, new opportunities for enhancing current decision-support systems will appear. In this scenario, two approaches are clearly identified. One focuses on automating multidimensional design, using semantic web artifacts, for example, existing ontologies. In this approach, data warehouses are (semi)automatically designed using available metadata and then populated with semantic web data. The other approach aims at analyzing large amounts of semantic web data using OLAP tools. In this chapter, we tackle the latter approach, which requires the definition of a precise vocabulary allowing to represent OLAP data on the semantic web. Over this vocabulary, multidimensional models and OLAP operations for the semantic web can be defined. Currently, there are two proposals in this direction. On the one hand, the data cube vocabulary (also denoted QB) follows statistical data models. On the other hand, the QB4OLAP vocabulary follows closely the classic multidimensional models for OLAP studied in this book. On the other hand, the QB4OLAP vocabulary follows 20 closely the classic multidimensional models for OLAP studied in this book.
KeywordsResource Description Framework Data Warehouse Data Cube SPARQL Query Aggregate Function
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