SM4MQ: A Semantic Model for Multidimensional Queries

Abstract

Abstract. On-Line Analytical Processing (OLAP) is a data analysis approach to support decision-making. On top of that, Exploratory OLAP is a novel initiative for the convergence of OLAP and the Semantic Web (SW) that enables the use of OLAP techniques on SW data. Moreover, OLAP approaches exploit different metadata artifacts (e.g., queries) to assist users with the analysis. However, modeling and sharing of most of these artifacts are typically overlooked. Thus, in this paper we focus on the query metadata artifact in the Exploratory OLAP context and propose an RDF-based vocabulary for its representation, sharing, and reuse on the SW. As OLAP is based on the underlying multidimensional (MD) data model we denote such queries as MD queries and define SM4MQ: A Semantic Model for Multidimensional Queries. Furthermore, we propose a method to automate the exploitation of queries by means of SPARQL. We apply the method to a use case of transforming queries from SM4MQ to a vector representation. For the use case, we developed the prototype and performed an evaluation that shows how our approach can significantly ease and support user assistance such as query recommendation.

OLAP Patterns: A pattern-based approach to multidimensional data analysis

“…In particular, the textual descriptions consist of a list of alias names for the pattern, a statement of the (analytical) problem tackled by the pattern, and a description of the solution. The context specification, in turn, generically describes the environment which the pattern can be applied in; it should be noted that the multidimensional query to be composed is not conceptually represented such as in Varga et al [18]. In particular, the context specification comprises a declaration of pattern parameters as well as constraints, which the enriched multidimensional model of a data warehouse must satisfy in order for the pattern to be applicable in the context of that data warehouse.…”

Analytical metadata modeling for next generation BI systems

“…An RDF ontological metamodel can significantly reduce the manual efforts for aligning different data sets via metadata. For instance, we can automatically identify query elements in different systems [25] and align them via metamodeling. Note that we propose an alternative to the traditional instance-based data integration.…”

“…In general, what is to be considered as an atomic element depends on the model instantiating the metamodel (e.g., a SPARQL operation can be part of a SPARQL query). A simple example of query (i.e., sm4am:UAList) instantiation for our motivating example (see Section 2) is illustrated in Figure 8, Section 6, while an example of a complete query model instantiating SM4AM can be found in [25]. Note that examples of remaining metamodel elements are analogous and extensive examples can be found in [39].…”