Efthimios Tambouris held his keynote “Multidimensional Open Government Data” at the CeDEM16.
Efthimios Tambouris is an Associate Professor of “Information Systems and eGovernment” at the Department of Applied Informatics at the University of Macedonia, Thessaloniki, Greece. Before that, he served at Research Centers CERTH/ITI and NCSR ‘Demokritos’ and the ICT Industry. Dr. Tambouris holds a Diploma in Electrical Engineering from the National Technical University of Athens (NTUA), Greece, and an MSc and PhD from Brunel University, UK. During the last sixteen years he has initiated, coordinated and participated in numerous EU-funded research projects mainly in the field of eGovernment. He is an expert for the European Commission and CEN. He is co-chair of the IFIP International Conference on eParticipation (ePart) and has more than 150 scientific publications.
CeDEM – the international Conference for e-Democracy and Open Government – brings together e-democracy, e-participation and open government specialists working in academia, politics, government and business to critically analyse the innovations, issues, ideas and challenges in the networked societies of the digital age. The CeDEM16 will be held from May 18th to May 20th 2016 at the Danube University Krems.
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Efthimios Tambouris, Applied Informatics Dpt at University of Macedonia (GR), on “Multidimensional Open Government Data”
There are various open data portals and a lot of conceptual research has been conducted. When examining data portals, it becomes apparent, that the vast majority of the data are numbers (demographic, statistical data etc.). Statistical analyses, visualisations and the development of apps are possible.
What’s new in the OGD portals environment?
New is the fact, that one has access to so much data. Searching for international data might though be not too easy, because this would have to be done using different data portals. Looking at internet usage vs. individuals’ level of internet skills reveals certain correlations. To take one example, data.gov.uk was selected with the objective to get data on unemployment. This showed, finding the data even on only one data portal can be quite difficult, as the result were over 1000 excel files, which would have to be all opened manually. Data being located in excel or csv files and being distributed makes reaching good results challenging.
Usually, being provided only with a number doesn’t suffice to understand its meaning, as it is dependent on the context as f.i. what is measured and to what place the measurement – which is expressed through the number – refers.
Introducing Data Cubes
Statistical data can be compared to data cubes encompassing different dimensions. To stick to the example, such a dimension could be the age of a person if we deal with the issue of unemployment. Data can be rolled up or drilled down (in the sense of summarizing and climbing up a hierarchy or reducing a dimension on the one hand or going down a hierarchy), diced and also sliced, as is shown visually.
One challenge is the knowledge necessary for expanding cubes with data of different cubes. These different cubes could encompass the same dimensions, but comprise different measures.
The research project Open Cube deals with the question of how publishers can publish their content in cubes and how users can make use of it.
Finding data stored in different countries’ portals
Through the internet, within one’s working environment a SPARQL query can be issued and an answer is transferred, provided that the data is stored in the RDF-format. This can also be applied with different sources, which would solve the problem of linking open data portals. With reference to Tim Berners Lee’s 5-star model, a portal publishing data in RDF should be assigned with four stars, while providing Linked Open Data is seen as 5-star-procedure. The RDF Data Cube Vocabulary allows to model multi-dimensional data as RDF-graphs.
Expander suggests improvements
There is a need for tools in order to start with your own data if you want to create statistical cubes and there are lots of tools, starting from XLS and CSV. New is the expander, which was added to the cube structure and the data (table view). A compatibility-check is done by the software and makes suggestions to supplement the data with f.i. newer data on the same issue. The tool supports different visualisation techniques and enables f.i. comparisons of data of the same topic from different regions (as f.i. employment data).
Further Information about Efthimios Tambouris: http://uom-gr.academia.edu/tambouris