Processing Linked Open Data Cubes

Abstract

Part 2: Open and Smart GovernmentInternational audienceA significant part of open data provided by governments and international organizations concerns statistics such as demographics and economic indicators. The real value, however, of open statistical data will unveil from performing analytics on top of combined datasets from disparate sources. Linked data provide the most promising technological paradigm to enable such analytics across the Web. Currently, however, relevant processes and tools do not fully exploit the distinctive characteristics of statistical data. The aim of this paper is to present a process that enables publishing statistical raw data as linked data, combining statistics from multiple sources, and exploiting them in data analytics and visualizations. Moreover, the capability of existing software tools to support the vision of linked statistical data analytics is evaluated. We anticipate that the proposed process will contribute to the development of a roadmap for future research and development in the area

Query Nesting, Assignment, and Aggregation in SPARQL 1.1

“…Colazzo et al [22] proposed a semantics for analytical queries and OLAP operators in the context of RDF; their work, however, does not provide a clear connection with the semantics of aggregation in SPARQL 1.1 and does not study the expressive power of the introduced operators. The RDF Cube specification [23] provides a vocabulary and specific guidelines for representing multidimensional data in RDF; practical aspects and first concrete implementations of the standard have been recently discussed in [33,35,55].…”

ICT tools for creating, expanding and exploiting statistical linked Open Data

“…This step also resulted in a lifecycle that describes the process that raw statistical data go through in order to create value based on linked data [18]. In particular, we consider that raw data go through a lifecycle that enables (a) creating, (b) expanding, and (c) exploiting LOSD.…”

“…In particular, we consider that raw data go through a lifecycle that enables (a) creating, (b) expanding, and (c) exploiting LOSD. Figure 2 presents these three phases of the lifecycle and the respective steps that can be followed in each phase [18].…”

Multidimensional Open Government Data

“…In the practical sense, introducing LOD qb in the everyday practices of open data publishers and consumers calls for a relevant multidimensional open data process. Figure 10 presents a proposed process that is divided into three phases: create, expand and exploit (or consume) (E. Tambouris et al, 2015b). The process prescribes the steps that raw data should go through in order to create value.…”

“…Figure 10: Statistical Open Data Process (from E. Tambouris et al, 2015b) Following this process, data producers can transform their raw data into RDF data cubes. In addition, they need to link their concepts (e.g., measures) with the same concepts in other OGD portals or elsewhere.…”