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Recently, mobile TV has been launched in several countries. While mobile TV integrates television contents into mobile phones, the most personal of communication devices, it becomes interesting to know how this feature will be used throughout the day and in varying contexts of everyday life. This paper presents empirical results on the use of mobile TV with different delivery mechanisms and both quantitative and qualitative results on how end-users prefer to use mobile TV contents in different situations. The data is based on ongoing empirical research in Finland in 2006 and 2007. The mobile TV services under study included both news and entertainment contents, and were tested in 3G, DVB-H and Wi-Fi networks using different delivery paradigms: broadcast, on-demand and download. To explore the use of different delivery methods and content consumption, we have developed a mobile TV service protoype, called Podracing. The analysis shows that users appreciated up-to-date information and information-rich media forms and contents especially for mobile news delivery. There was high demand for only the latest news on mobiles. The real-time property was considered important. Most of the users looked at the headlines or followed the news several times a day -much more often than the traditional TV and news prime times would allow.

Abstract. TV has often been regarded primarily as a traditional family medium [8], because it is mainly watched at home and used as a basis for interaction with others. Now that the mobile phone, which people seem to experience as a personal communication device, has developed functionalities peculiar to mass media, including a TV feature, it is interesting to know how these functionalities will be used throughout the day and in different everyday life contexts. Will mobile TV used mostly on the go or as an additional media at home? Will it become as a highly individualized media format or will the watching experience typically be shared with others? This paper examines the users' mobile TV choices in different everyday life contexts. The data is based on ongoing empirical research in Finland in 2006. The tested mobile TV services included both news and entertainment contents, and were tested in 3G and DVB-H networks.

DVB-H offers a new platform for IP-based services and contributes to universal access. There are many challenges of providing multimedia TV experience for DVB-H users anywhere anytime. One big challenge is due to the strict reception conditions and mobility. For example, when a mobile receiver enters a new cell with different transmission frequency, the receiver must accomplish a seamless handover process in order to continue the selected service without an interrupt. Furthermore, it is possible to build DVB-H services into existing DVB-T networks that provide even less possibilities to optimize the transmission for mobile use. This paper presents a novel handover approach to IP streams on a DVB-H network. We handover IP streams by switching between the DVB-H and the UMTS networks without doing any frequency scan. We have successfully tested the service handover approach. The validation has shown that there was no any packet loss during a handover process. The method also provides technology about efficient sharing of wireless network infrastructure with DVB-H, and makes it more feasible to include DVB-H services into existing DVB-T networks and can be used for reducing the time for changing a channel (zapping time).

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Clustering has been recognized as one of the important tasks in data mining. One important class of clustering is distance based method. To reduce the computational and storage burden of the classical clustering methods, many distance based hybrid clustering methods have been proposed. However, these methods are not suitable for cluster analysis in dynamic environment where underlying data distribution and subsequently clustering structures change over time. In this paper, we propose a distance based incremental clustering method, which can find arbitrary shaped clusters in fast changing dynamic scenarios. Our proposed method is based on recently proposed al-SL method, which can successfully be applied to large static datasets. In the incremental version of the al-SL (termed as IncrementalSL), we exploit important characteristics of al-SL method to handle frequent updates of patterns to the given dataset. The IncrementalSL method can produce exactly same clustering results as produced by the al-SL method. To show the effectiveness of the IncrementalSL in dynamically changing database, we experimented with one synthetic and one real world datasets.

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