Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 112-118).

Mobile social applications have emerged in recent years. They explore social connections among mobile users in a variety of novel scenarios, including friend finding, message routing, and content sharing. However, efficiently supporting resource-demanding delay-sensitive streaming applications on the mobile platform remains a significant challenge. Research on such topics will naturally widen the usage of mobile social applications. The solutions to the challenges will provide suggestion on many related work. It is interesting and valuable to explore the system performance and users’ experience in such scenarios. Furthermore, users’ concern about social network is also significant to develop a mobile social network application. It is important to detect users’ strategies to communicate with others. That influences the network topologies and provides biased connections. The strategy consists of various of aspects, most of which are the user preference and user social attributes. Focusing on this meaningful research field, we study collaborative VoD-type streaming of short videos among small groups of mobile users, so as to effectively exploit their social relationships. Such an application can be practically set in a number of usage scenarios, including streaming of introductory video clips of exhibition items to visitors’ mobile devices, such as in a museum. We analyze users’ behavior strategies based on their social preference and social attributes. We design SMS, an architecture that engineers such Streaming over Mobile Social networks. SMS constructs a collaborative streaming overlay by carefully inspecting social connections among users and infrastructure characteristics of Bluetooth technologies. To improve the performance, we analyze the scatternet structure of Bluetooth technology and propose appropriate scatternet structure in our system. We evaluate our design based on prototype implementation on the Android platform, as well as on a large emulation testbed. The results obtained indicate that we are able to achieve a well-performed streaming system in a mobile social network. / published_or_final_version / Computer Science / Master / Master of Philosophy

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published_or_final_version / abstract / toc / Computer Science and Information Systems / Doctoral / Doctor of Philosophy

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Classification under streaming data conditions requires that the machine learning approach operate interactively with the stream content. Thus, given some initial machine learning classification capability, it is not possible to assume that the process `generating' stream content will be stationary. It is therefore necessary to first detect when the stream content changes. Only after detecting a change, can classifier retraining be triggered. Current methods for change detection tend to assume an entropy filter approach, where class labels are necessary. In practice, labeling the stream would be extremely expensive. This work proposes an approach in which the behavior of GP individuals is used to detect change without} the use of labels. Only after detecting a change is label information requested. Benchmarking under three computer network traffic analysis scenarios demonstrates that the proposed approach performs at least as well as the filter method, while retaining the advantage of requiring no labels.

Recent research addresses the problem of data-stream mining to deal with applications that require processing huge amounts of data such as sensor data analysis and financial applications. Data-stream mining algorithms incorporate special provisions to meet the requirements of stream-management systems, that is stream algorithms must be online and incremental, processing each data record only once (or few times); adaptive to distribution changes; and fast enough to accommodate high arrival rates. We consider the problem of data-stream classification, introducing an online and incremental stream-classification ensemble algorithm, Streaming Random Forests, an extension of the Random Forests algorithm by Breiman, which is a standard classification algorithm. Our algorithm is designed to handle multi-class classification problems. It is able to deal with data streams having an evolving nature and a random arrival rate of training/test data records. The algorithm, in addition, automatically adjusts its parameters based on the data seen so far. Experimental results on real and synthetic data demonstrate that the algorithm gives a successful behavior. Without losing classification accuracy, our algorithm is able to handle multi-class problems for which the underlying class boundaries drift, and handle the case when blocks of training records are not big enough to build/update the classification model. / Thesis (Ph.D, Computing) -- Queen's University, 2008-07-15 16:12:33.221

The past decade has witnessed tremendous growth of peer-to-peer (P2P) video streaming applications on the Internet. For these applications, playback smoothness and timeliness are the two most important aspects of users' viewing experiences, whereas the amount of traffic is Internet service providers' main concern. According to the playback delay, video streaming can be classified into on-demand streaming, live streaming, and interactive streaming. P2P live streaming applications typically have an arbitrary number of users, tens of seconds of playback delay, and a high packet delivery rate, but their heavy traffic incurs great financial expenditure and threatens the quality of other services. Interactive streaming applications usually have a small group size, several hundreds of milliseconds of playback delay, and reasonable traffic volume, but cannot achieve a high packet delivery rate. The goal of this thesis is to study traffic locality and reliable delivery of packets in large-scale live streaming and small-scale interactive streaming applications, while keeping the playback delay well below the targeted applications' limits. For P2P live streaming applications, we first identify "typical" schemes from existing P2P live streaming schemes, investigate packet propagation behavior and the impact of neighboring strategies on system performance, and then propose innovative schemes that take both users' viewing experience and traffic locality into consideration. We show that the network-driven tree-based schemes with the swarming technique as a re-transmission error-correction mechanism are superior to the data-driven swarm-based or tree-based schemes, and a properly designed tree-based scheme can localize the traffic while maintaining a high packet delivery rate. For interactive streaming applications, we analyze the efficacy of systematic forward error-correction (FEC) codes against the bursty errors of Internet links when using peers to provide multiple one-hop paths between two communication parties. We find that although using peers for path diversity often results in a lower post-FEC packet loss ratio, some conditions do apply. The interplay of a number of factors, such as the Internet links' error ratio and burst length and the coding parameters, determines the performance of FEC. We provide guidelines and computation methods to determine whether the use of peers for path diversity can be justified. / Thesis (Ph.D, Computing) -- Queen's University, 2012-04-26 15:20:35.555

Digitaliseringen under 2000-talet har förändrat många branscher. Utbredningen av illegal fildelning är en av de utmaningar som dykt upp, utbredd bland i övrigt laglydiga människor. Samtidigt sker en framväxt av nya sätt att konsumera på internet, som tar avstamp i fildelningstekniken men fungerar inom lagliga ramar. Uppsatsen har mot bakgrund av detta drivits av att undersöka hur fildelning motiveras: Hur ser argumenten ut för och emot illegal fildelning?   I detta arbete undersöks de argument som används för att rättfärdiga fildelning av musik, genom en kvalitativ argumentationsanalys, kombinerad med Boltanski Thévenots teori om rättfärdigande och värdeordningar. Materialet utgörs av en diskussionstråd på internetforumet Flashback om fildelningssajten The Pirate Bay och streamingtjänsten Spotify. Dessutom undersöks också rättfärdigandet av de streamingtjänster som vuxit fram som alternativ till den illegala fildelningen.   Resultatet visar att både fildelning och användandet av streamingtjänster motiveras av pragmatism: smidighet och effektivitet appellerar till användarna mer än tradition och känsla. Dessutom uppvisas en uppluckring i synen på musik som handelsvara: Den ständiga tillgången har förändrat viljan att betala för enskilda album och låtar.