Dimensional Enrichment of Statistical Linked Open Data

Abstract

On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process.

SM4MQ: A Semantic Model for Multidimensional Queries

“…As the former vocabulary was primarily designed for statistical data sets, the latter one was proposed to extend QB with necessary concepts to fully support OLAP. A detailed discussion on this is presented in [24] where it is also explained how existing QB data sets can be enriched with the QB4OLAP semantics. Thus, in the present paper we consider QB4OLAP for the representation of the MD data on the SW and Figure 2 illustrates how the MD schema from Figure 1 can be represented with QB4OLAP.…”

“…The semantics of MD queries captured at the conceptual abstraction level is understandable even for non-technical OLAP end-users [10,24]. It can automatically be transformed into different data structures (e.g., vectors) via SPARQL and thereby benefit from algorithms working over the related structures (e.g., computing cosine similarity between vectors).…”

“…However, although needed for the context of Exploratory OLAP [1], an MD query model is still missing. By now, most of the efforts have been devoted to the schema modeling with vocabularies such as QB4OLAP (see [24]). Thus, the present paper proposes an RDF-based model to support sharing and reuse of MD queries on the SW that as well facilitates their exploitation.…”

“…Then, metadata models of specific systems are defined at the model level instantiating one or more AM artifacts. For instance, the schema artifact for Exploratory OLAP can be represented using the QB4OLAP vocabulary to conform data to a multidimensional (MD) data model for OLAP on the SW [24]. QB4OLAP further enables running of MD queries to perform OLAP on the SW [25].…”

QB2OLAP: Enabling OLAP on Statistical Linked Open Data

“…Similarly, aggregate functions are also component properties that are linked to the DSD 5 Some data sets include a Label, although there is no guarantee about this. 6 http://purl.org/qb4olap/cubes via the qb4o:aggregateFunction property associating measures with aggregate functions. Moreover, QB4OLAP defines the qb4o:cardinality property that represents the cardinality of the relationship between a fact and a dimension level.…”