Open Statistics: The Rise of a new Era for Open Data?
A large part of open data concerns statistics, such as demographic, economic and social data (henceforth referred to as Open Statistical Data, OSD). In this paper we start by introducing open data fragmentation as a major obstacle for OSD reuse. We proceed by outlining data cube as a logical model for structuring OSD. We then introduce Open Statistics as a new area aiming to systematically study OSD. Open Statistics reuse and extends methods from diverse fields like Open Data, Statistics, Data Warehouses and the Semantic Web. In this paper, we focus on benefits and challenges of Open Statistics. The results suggest that Open Statistics provide benefits not present in any of these fields alone. We conclude that in certain cases OSD can realise the potential of open data.
Linked Open Cube Analytics Systems: Potential and Challenges IEEE Intelligent Systems
Linked Open Cube Analytics (LOCA) systems enable the performance of analytics on top of multiple open statistical data (OSD) that reside in disparate portals. We present OSD's potential and highlight the problems hampering its integration and reuse. To overcome these problems, we introduce an approach for OSD integration. The proposed approach capitalizes on the data cube model and linked data technologies to enable unified access to multiple OSD published in disparate portals. Finally, we present an online analytical processing (OLAP) browser for linked data cubes as a proof of concept of LOCA systems. Throughout this article, we also outline the challenges that need to be addressed for the wide adoption of LOCA systems.
E. Kalampokis, E. Tambouris, K. Tarabanis (2016) Linked Open Cube Analytics Systems: Potential and Challenges IEEE Intelligent Systems, Vol. 31, No.5, pp.89-92 http://dx.doi.org/10.1109/MIS.2016.82
Linked data cubes: Research results so far
During the last years a growing body of literature studied Linked Data Cubes. The objective of this paper is to accumulate this body of knowledge and provide a preliminary analysis of the research re- sults in the area so far. Towards this end, we systematically reviewed the scientific literature to identify relevant studies. These studies were anal- ysed and synthesised in the form of a proposed conceptual framework, which was thereafter applied to further analyse this literature, hence gaining new insights into the field. The framework comprises three di- mensions, namely category of contribution, step of data analysis, and application area. The application of the framework resulted in interest- ing findings. For example, the majority of the contributions that focus on publishing linked data cubes are cases while the majority of the ex- ploitation contributions are software tools. Moreover, integration of data cubes remains largely unexamined in the literature. This paper, however, does not present the final results of the analysis of the literature as this is still an ongoing activity.
A. Karamanou, E. Kalampokis, E. Tambouris, K. Tarabanis (2016) Linked data cubes: Research results so far, SemStats2016 in conjuction with the 15th International Semantic Web Conference (ISWC2016), 17-21 October 2016, Kobe, Japan, CEUR-WS
Open Statistical Data: Potential and Challenges
Opening up data is a political priority worldwide. Linked open data is considered as the most mature technology for publishing and reusing open data. A large number of open data is numerical and actually concerns statistics. In the literature, statistical data have been heavily studied using the data cube model. Recently, ICT tools have emerged aiming to exploit linked open data technologies for providing advanced visualizations and analytics of open statistical data residing in geographically dispersed open data portals. The aim of this panel is to discuss the potential and challenges of open statistical data.
E. Tambouris, M. Janssen, E. Kalampokis, B. Roberts, P. Hermans, J. Whyte, T. Alcorn, K. Tarabanis (2016) Open Statistical Data: Potential and Challenges, Scholl, H. et al. (Eds.): Electronic Government and Electronic Participation, pp. 407-408, IOS Press http://doi.org/10.3233/978-1-61499-670-5-407