Widget-based Exploration of Linked Statistical Data Spaces

Abstract

Today, public statistical data plays an increasingly important role both in public policy formation and as a facilitator for informed decision-making in the private sector. In line with the increasing adoption of open data policies, the amount of data published by governments and organizations on the web is growing rapidly. To increase the value of such data, the W3C recommends the RDF Data Cube Vocabulary to facilitate the publication of data in a more structured and interlinked manner. Although important first steps toward building a web of statistical Linked Datasets have been made, providing adequate facilities for end users to interactively explore and make use of the published data remains an unresolved challenge. This paper presents a widgetbased approach to deal with this issue. In particular, we introduce a mashup platform that allows users lacking advanced skills and knowledge of Semantic Web technologies to interactively analyze datasets through widget compositions and visualizations. Furthermore, we provide mechanisms for the interconnection of datasets to support sophisticated knowledge extraction.

Toward a statistical data integration environment

“…Steps i -iii are performed automatically by a data source analysis algorithm described in our previous work [7]. The last step uses the geographical area and temporal value mapping algorithms to automatically generate consolidated values.…”

Visualizing statistical linked knowledge for decision support

“…Such a lifecycle can be expressed as a series of visualization pipelines [13,26], which requires developers to follow certain workflows. When visualizing statistical LD, such workflows will necessarily include both LD tasks (selection of indicators, ontology alignment, etc.)…”

“…Another paper related to the same project [24] presents the Linked Data Query Wizard which uses a table-based approach to selecting query results from QB datasets, and classic chart types or mind maps to visualize the results. BaLam Do [13] developed a visualization pipeline focused on creating Linked Widgets like lines, bars, pies, and especially maps, from QB datasets. He also identified two main problems for statistical LD visualizations: a) the challenge of analyzing and aligning multiple datasets due to the fact that most publishers use the QB vocabulary as a guideline rather than as a specification and almost always come up with some changes to it; b) the challenge of creating tools for consuming statistical LD.…”

“…Helmich [22] implements this model in Payola for visualizing the Czech LOD cloud. Ba-Lam Do's Linked Widgets platform [13] is a pipeline for creating mashups.…”

“…9 The idea behind LDPs is the delivery of data in various formats using REST APIs (Application Programming Interfaces following the Representational State Transfer standard). Some platforms also allow to build full-featured interfaces that can contain maps or pictures, typically using templating solutions such as Velocity, 10 Elda, 11 Carbon LDP, 12 Apache Marmotta, 13 Graphity, 14 , LDP4j [19] and Virtuoso 15 are several exponents of this trend. Many of the applications developed following LDP best practices 16 include maps or other types of visualizations.…”