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with at least one more European country. The French data will also be jointly analyzed
with the German data in the forthcoming research work, so that our work can have a
better representation of European countries.
4 Conclusion
We present the use of OSM to analyze the popularity in the Mobile Cloud. We use the
German data between 2011 and 2012 as the example and compare the actual values,
expected values and risk-control rates in all the datasets. We explain the use of OSM
to process datasets and the key statistics involved and their interpretations. We explain
all the key results and show that there is a decline in the popularity due to the econom-
ic downturn and also competitions from other mobile systems.
Key results include the beta, standard error, Durbin-Watson, p-values, mean square
errors and R-squared values. We confirm that all these key figures fulfil the criteria for
the OSM analysis. The use of 3D Visualization can expose unexploited data analysis
and also ensure the stakeholders can interpret analysis easily. OSM is an innovative
approach which can be adapted in other research projects, in different disciplines and
in other case studies. We will demonstrate how OSM can be used in other disciplines
and also other European countries to study the popularity in the use of Mobile Cloud.
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