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.
The second line of research tries to overcome the drawbacks of the first one, exploring data models and tools that allow publishing and performing OLAP-like analysis directly over SW MD data. Terms like @cite_1 , @cite_0 , , or even , refer to the capability of incorporating situational data into the decision process with little or no intervention of programmers or designers. The web, and in particular the SW, is considered as a large source of data that could enrich decision processes. Abell ' @cite_1 present a framework to support self-service BI, based on the notion of , i.e., MD cubes that can be dynamically extended both in their schema and their instances, and in which data and metadata can be associated with quality and provenance annotations.
@cite_0 @cite_1
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Traditional business intelligence has focused on creating dimensional models and data warehouses, where after a high modeling and creation cost structurally similar queries are processed on a regular basis. So called "ad-hoc" queries aggregate data from one or several dimensional models, but fail to incorporate other external information that is not considered in the pre-defined data model. We focus on a different kind of business intelligence, which spontaneously correlates data from a company’s data warehouse with "external" information sources that may come from the corporate intranet, are acquired from some external vendor, or are derived from the internet. Such situational applications are usually short-lived programs created for a small group of users with a specific business need. We will showcase the state-of-the-art for situational applications as well as the impact of Web 2.0 for these applications. We will also present examples and research challenges that the information management research community needs to address in order to arrive at a platform for Situational Business Intelligence. Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not owned and controlled by the decision maker; their search, extraction, integration, and storage for reuse or sharing should be accomplished by decision makers without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges; the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which situational data and metadata are associated with quality and provenance annotations.