CubeViz.js: A Lightweight Framework for Discovering and Visualizing RDF Data Cubes

In this paper we present CubeViz.js, the successor of CubeViz, as an approach for lightweight visualization and exploration of statistical data using the RDF Data Cube vocabulary. In several use cases, such as the European Unions Open Data Portal, in which we deployed CubeViz, we were able to gather various requirements that eventually led to the decision of reimplementing CubeViz as JavaScript-only application. As part of this paper we showcase major functionalities of CubeViz.js and its improvements in comparison to the prior version

Visualizing Statistical Linked Knowledge for Decision Support

In a global and interconnected economy, decision makers often need to consider information from various domains. A tourism destination manager, for example, has to correlate tourist behavior with financial and environmental indicators to allocate funds for strategic long-term investments. Statistical data underpins a broad range of such cross-domain decision tasks. A variety of statistical datasets are available as Linked Open Data, often incorporated into visual analytics solutions to support decision making. What are the principles, architectures, workflows and implementation design patterns that should be followed for building such visual cross-domain decision support systems. This article introduces a methodology to integrate and visualize cross-domain statistical data sources by applying selected RDF Data Cube (QB) principles. A visual dashboard built according to this methodology is presented and evaluated in the context of two use cases in the tourism and telecommunications domains

Visualization of heterogeneous data

Abstract — Both the Resource Description Framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template. Index Terms—Data integration, RDF, attribute inference.

Exploring meta-analysis for historical corpus linguistics based on linked data

Empirical work on English historical corpus linguistics is plentiful but fragmented, and some of it is hard to come by. This paper proposes a solution for making it more accessible and reusable for meta-analysis. We present an online Language Change Database (LCD), which provides comparative, real-time baseline data from earlier corpus-based studies. LCD entries summarize the findings and include numerical data from the articles. We discuss the LCD from the perspective of database design and linked data management. Furthermore, we illustrate the reuse of LCD data through a meta-analysis of the history of English connectives. For this purpose, we have developed an application called the LCD Aggregated Data Analysis workbench (LADA). We show how researchers can use LADA to filter, refine and visualize LCD data. Thus we are paving the way for a future where both research results and research data are regularly available for verification, validation and re-use.Peer reviewe

Multi-Level Visual Tours of Weather Linked Data

International audienceThe recent trend of adopting linked-data principles to integrate and publish semantically described open data using W3C standards has led to a large amount of available resources. In particular, meteorological sensor data have been uplifted into public weather-focused RDF graphs, such as WeKG-MF which offers access to a large set of meteorological variables described through spatial and temporal dimensions. Nevertheless, these resources include huge numbers of raw observations that are tedious to explore by lay users. In this article, we aim at providing them with visual exploratory "tours", benefiting from RDF data cubes to present high-level aggregated views together with on-demand fine-grained details through a unified Web interface

Interactive Multidimensional Modeling of Linked Data for Exploratory OLAP

Exploratory OLAP aims at coupling the precision and detail of corporate data with the information wealth of LOD. While some techniques to create, publish, and query RDF cubes are already available, little has been said about how to contextualize these cubes with situational data in an on-demand fashion. In this paper we describe an approach, called iMOLD, that enables non-technical users to enrich an RDF cube with multidimensional knowledge by discovering aggregation hierarchies in LOD. This is done through a user-guided process that recognizes in the LOD the recurring modeling patterns that express roll- up relationships between RDF concepts, then translates these patterns into aggregation hierarchies to enrich the RDF cube. Two families of aggregation patterns are identified, based on associations and generalization respectively, and the algorithms for recognizing them are described. To evaluate iMOLD in terms of efficiency and effectiveness we compare it with a related approach in the literature, we propose a case study based on DBpedia, and we discuss the results of a test made with real users