Today's mobile data applications aspire to deliver services to a user anywhere – anytime while fulfilling his Quality of Service (QoS) requirements. However, the success of the service delivery heavily relies on the QoS offered by the underlying networks. As the services operate in a heterogeneous networking environment, we argue that the generic information about the networks' offered-QoS may enable an anyhow mobile service delivery based on an intelligent (proactive) selection of ‘any' network available in the user's context (location and time). Towards this direction, we develop a QoS-predictions service provider, which includes functionality for the acquisition of generic offered-QoS information and which, via a multidimensional processing and history-based reasoning, will provide predictions of the expected offered-QoS in a reliable and timely manner. We acquire the generic QoS-information from distributed mobile services' components quantitatively (actively and passively) measuring the applicationlevel QoS, while the reasoning is based on statistical data mining and pattern recognition techniques.
This article analyses the capacity of radical right parties to attract support from un-ion members in recent elections in Western Europe. It is argued that unionized vot-ers resist the appeals of the radical right better than non-union members. Using data from the European Social Survey 2010–2016, the article shows that union members are overall less likely to vote for the radical right than non-union members. Even though it is found that unionized working-class and middle-class voters are less like-ly to vote radical right than their non-unionized peers in the pooled sample, it is also observed that these subgroups of unionized voters and especially unionized working-class voters are not immune to radical right voting in all the countries analysed. The article thus indicates a growing capacity of the radical right to attract unionized working-class segments of the electorate in some countries and to directly compete with left parties for these voters.
The average mobile device includes several sensors as a standard feature. Moreover, it roams with its owner, and can be used to collect context information on their behalf. It is often vital to collect data in order to create realistic models that might help us understand and predict the world. However, sharing personal data increases the chance of a user’s privacy being compromised by revealing their identity. In this thesis we show that most of the sensor data on a device should be handled with caution due to their potential to be a privacy threat and propose solutions for service self-provisioning for measuring location tracking information through unaided triangulation, and location context by using cell ID traces. When data absolutely needs to reach a third party, we show that opportunistic mixing strategies can be effective in anonymizing the source of the data.
