With mobile devices being today one of the main sources of Internet traffic, and the increasing amount of mobile services and applications, the importance of monitoring quality of service for this platform cannot be ignored. Such is the motivation behind the Adkintun Mobile project, attempting to characterize mobile QoS through passive monitoring of devices. The first phase of our project showed great results for our passive approach, however, we concluded that some active testing may be necessary for a more accurate characterization of service quality, but the device context is needed to best establish when to test. In this work we describe our efforts towards context-based active testing, including the development of our monitoring middleware, that through recording of status changes for different aspects of the system in the device, will allow us (and other developers) to clearly identify the context when QoS issues occur.

The Open Government Directive is making US government data available via websites such as Data.gov for public access. In this paper, we present a Semantic Web based approach that incrementally generates Linked Government Data (LGD) for the US government. In focusing on the trade-off between high quality LGD generation (requiring non-trivial human expert input) and massive LGD generation (requiring low human processing cost), our work is highlighted by the following features: (i) supporting low-cost and extensible LGD publishing for massive government data; (ii) using Social Semantic Web (Web3.0) technologies to incrementally enhance published LGD via crowdsourcing, and (iii) facilitating mash-ups by declaratively reusing cross-dataset mappings which usually are hard-coded in applications.