The web is changing the way in which data warehouses are designed, used, and queried. With the advent of initiatives such as Open Data and Open Government, organizations want to share their multidimensional data cubes and make them available to be queried online. The RDF data cube vocabulary (QB), the W3C standard to publish statistical data in RDF, presents several limitations to fully support the multidimensional model. The QB4OLAP vocabulary extends QB to overcome these limitations, allowing to im- plement the typical OLAP operations, such as rollup, slice, dice, and drill-across using standard SPARQL queries. In this paper we introduce a formal data model where the main object is the data cube, and define OLAP operations using this model, independent of the underlying representation of the cube. We show then that a cube expressed using our model can be represented using the QB4OLAP vocabulary, and finally we provide a SPARQL implementation of OLAP operations over data cubes in QB4OLAP.
K " a mpgen and Harth @cite_11 study the extraction of statistical data published using the QB vocabulary into a MD database. The authors propose a mapping between the concepts in QB and a MD data model, and implement these mappings via SPARQL queries. There are four main phases in the proposed methodology: (1) Extraction, where the user defines relevant data sets which are retrieved from the web and stored in a local triple store. Then, SPARQL queries are performed over this triple store to retrieve metadata on the schema, as well as data instances; (2) Creation of a relational representation of the MD data model, using the metadata retrieved in the previous step, and the population of this model with the retrieved data; (3) Creation of a MD model to allow OLAP operations over the underlying relational representation. Such model is expressed using XML for Analysis (XMLA) http: xmlforanalysis.com , which allows the serialization of MD models and is implemented by several OLAP clients and servers; (4) Specification of queries over the DW, using OLAP client applications.
@cite_11
2062345051
The amount of available Linked Data on the Web is increasing, and data providers start to publish statistical datasets that comprise numerical data. Such statistical datasets differ significantly from the currently predominant network-style data published on the Web. We explore the possibility of integrating statistical data from multiple Linked Data sources. We provide a mapping from statistical Linked Data into the Multidimensional Model used in data warehouses. We use an extract-transform-load (ETL) pipeline to convert statistical Linked Data into a format suitable for loading into an open-source OLAP system, and thus demonstrate how standard OLAP infrastructure can be used for elaborate querying and visualisation of integrated statistical Linked Data. We discuss lessons learned from three experiments and identify areas which require future work to ultimately arrive at a well-interlinked set of statistical data from multiple sources which is processable with standard OLAP systems.