Efficient Analytical Queries on Semantic Web Data Cubes

The amount of multidimensional data published on the semantic web (SW) is constantly increasing, due to initiatives such as Open Data and Open Government Data, among other ones. Models, languages, and tools, that allow to obtain valuable information efficiently, are thus required. Multidimensional data are typically represented as data cubes, and exploited using Online Analytical Processing (OLAP) techniques. The RDF Data Cube Vocabulary, also denoted QB, is the current W3C standard to represent statistical data on the SW.Since QB does not include key features needed for OLAP analysis, in previous work we have proposed an extension, denoted QB4OLAP, to overcome this problem without the need of modifying already published data. Once data cubes are represented on the SW, we need tools to analyze them. However, writing efficient analytical queries over SW cubes demands a deep knowledge of RDF and SPARQL. These skills are not common in typical analytical users. Also, OLAP languages like MDX are far from being easily understood by the final user. The lack of friendly tools to exploit multidimensional data on the SW is a barrier that needs to be broken to promote the publication of such data. We address this problem in this paper. Our approach is based on allowing analytical users to write queries using OLAP operations over cubes, without dealing with SW standards. For this, we devised CQL (standing for Cube Query Language), a simple, high-level query language that operates over cubes. Using the metadata provided by QB4OLAP, we translate CQL queries into SPARQL. Then, we propose query improvement strategies to produce efficient SPARQL queries, adapting SPARQL query optimization techniques. We evaluate our approach using the Star-Schema benchmark, showing that our proposal outperforms others. A web application that allows querying SW data cubes using CQL, completes our contributions.