XML stream applications bring the challenge of efficiently processing queries on sequentially accessible token-based data streams. The automaton paradigm is naturally suited for pattern retrieval on tokenized XML streams, but requires patches for implementing the filtering or restructuring functionalities common for the XML query languages. In contrast, the algebraic paradigm is well-established for processing self-contained tuples. However, it does not traditionally support token inputs. This dissertation proposes a framework called Raindrop, which accommodates both the automaton and algebra paradigms to take advantage of both. First, we propose an architecture for Raindrop. Raindrop is an algebra framework that models queries at different abstraction levels. We represent the token-based automaton computations as an algebraic subplan at the high level while exposing the automaton details at the low level. The algebraic subplan modeling automaton computations can thus be integrated with the algebraic subplan modeling the non-automaton computations. Second, we explore a novel optimization opportunity. Other XML stream processing systems always retrieve all the patterns in a query in the automaton. In contrast, Raindrop allows a plan to retrieve some of the pattern retrieval in the automaton and some out of the automaton. This opens up an automaton-in-or-out optimization opportunity. We study this optimization in two types of run-time environments, one with stable data characteristics and one with fluctuating data characteristics. We provide search strategies catering to each environment. We also describe how to migrate from a currently running plan to a new plan at run-time. Third, we optimize the automaton computations using the schema knowledge. A set of criteria are established to decide what schema constraints are useful to a given query. Optimization rules utilizing different types of schema constraints are proposed based on the criteria. We design a rule application algorithm which ensures both completeness (i.e., no optimization is missed) and minimality (i.e., no redundant optimization is introduced). The experimentations on both real and synthetic data illustrate that these techniques bring significant performance improvement with little overhead.

"As streaming techniques and wireless access networks become more widely deployed, a streaming multimedia connection with the "last mile" being a wireless network is becoming increasingly common. However, since current streaming techniques are primarily designed for wired networks, streaming multimedia applications can perform poorly in wireless networks. Recent research has shown that the wireless network conditions, such as the wireless link layer rate adaptation, contending traffic, and interference can significantly degrade the performance of streaming media applications. This performance degradation includes increased multimedia frame losses and lower image quality caused by packet loss, and multiple rebuffering events that stop the media playout. This dissertation presents the model, design, implementation and evaluation of an application layer solution for improving streaming multimedia application performance in IEEE 802.11 wireless networks by using enhanced bandwidth estimation techniques. The solution includes two parts: 1) a new Wireless Bandwidth estimation tool (WBest) designed for fast, non-intrusive, accurate estimation of available bandwidth in IEEE 802.11 networks, which can be used by streaming multimedia applications to improve the performance in wireless networks; 2) a Buffer and Rate Optimization for Streaming (BROS) algorithm using WBest to guide the streaming rate selection and initial buffer optimization. WBest and BROS are implemented and incorporated into an emulated streaming client-server system, Emulated Streaming (EmuS), in Linux and evaluated under a variety of wireless conditions. The evaluations show that with WBest and BROS, the performance of streaming multimedia applications in wireless networks can be significantly improved in terms of multimedia frame loss, rebuffer events and buffer delay."

" More and more researchers are focusing on the management, querying and pattern mining of streaming data. The visualization of streaming data, however, is still a very new topic. Streaming data is very similar to time-series data since each datapoint has a time dimension. Although the latter has been well studied in the area of information visualization, a key characteristic of streaming data, unbounded and large-scale input, is rarely investigated. Moreover, most techniques for visualizing time-series data focus on univariate data and seldom convey multidimensional relationships, which is an important requirement in many application areas. Therefore, it is necessary to develop appropriate techniques for streaming data instead of directly applying time-series visualization techniques to it. As one of the main contributions of this dissertation, I introduce a user-driven approach for the visual analytics of multivariate data streams based on effective visualizations via a combination of windowing and sampling strategies. To help users identify and track how data patterns change over time, not only the current sliding window content but also abstractions of past data in which users are interested are displayed. Sampling is applied within each single time window to help reduce visual clutter as well as preserve data patterns. Sampling ratios scheduled for different windows reflect the degree of user interest in the content. A degree of interest (DOI) function is used to represent a user's interest in different windows of the data. Users can apply two types of pre-defined DOI functions, namely RC (recent change) and PP (periodic phenomena) functions. The developed tool also allows users to interactively adjust DOI functions, in a manner similar to transfer functions in volume visualization, to enable a trial-and-error exploration process. In order to visually convey the change of multidimensional correlations, four layout strategies were designed. User studies showed that three of these are effective techniques for conveying data pattern changes compared to traditional time-series data visualization techniques. Based on this evaluation, a guide for the selection of appropriate layout strategies was derived, considering the characteristics of the targeted datasets and data analysis tasks. Case studies were used to show the effectiveness of DOI functions and the various visualization techniques. A second contribution of this dissertation is a data-driven framework to merge and thus condense time windows having small or no changes and distort the time axis. Only significant changes are shown to users. Pattern vectors are introduced as a compact format for representing the discovered data model. Three views, juxtaposed views, pattern vector views, and pattern change views, were developed for conveying data pattern changes. The first shows more details of the data but needs more canvas space; the last two need much less canvas space via conveying only the pattern parameters, but lose many data details. The experiments showed that the proposed merge algorithms preserves more change information than an intuitive pattern-blind averaging. A user study was also conducted to confirm that the proposed techniques can help users find pattern changes more quickly than via a non-distorted time axis. A third contribution of this dissertation is the history views with related interaction techniques were developed to work under two modes: non-merge and merge. In the former mode, the framework can use natural hierarchical time units or one defined by domain experts to represent timelines. This can help users navigate across long time periods. Grid or virtual calendar views were designed to provide a compact overview for the history data. In addition, MDS pattern starfields, distance maps, and pattern brushes were developed to enable users to quickly investigate the degree of pattern similarity among different time periods. For the merge mode, merge algorithms were applied to selected time windows to generate a merge-based hierarchy. The contiguous time windows having similar patterns are merged first. Users can choose different levels of merging with the tradeoff between more details in the data and less visual clutter in the visualizations. The usability evaluation demonstrated that most participants could understand the concepts of the history views correctly and finished assigned tasks with a high accuracy and relatively fast response time. "

Many modern applications process queries over unbounded streams of data. These applications include tracking financial data from international markets, intrusion detection in networks, monitoring remote sensors, and monitoring patients vital signs. These data streams arrive in real time, are unbounded in length and have unpredictable arrival patterns due to external uncontrollable factors such as network congestion or weather in the case of remote sensors. This thesis presents a novel technique for adapting the execution of stream queries that, to my knowledge, is not present in any other continuous query system to date. This thesis hypothesizes that utilizing a single scheduling algorithm to execute a continuous query, as is employed in other state-of-the-art continuous query systems, is not sufficient because existing scheduling algorithms all have inherent flaws or tradeoffs. Thus, one scheduling algorithm cannot optimally meet an arbitrary set of Quality of Service (QoS) requirements. Therefore, to meet unique features of specific monitoring applications, an adaptive strategy selector guidable by QoS requirements was developed. The adaptive strategy selector monitors the effects of its behavior on its environment through a feedback mechanism, with the aim of exploiting previously beneficial behavior and exploring alternative behavior. The feedback mechanism is guided by qualitatively comparing how well each algorithm has met the QoS requirements. Then the next scheduling algorithm is chosen by spinning a roulette wheel where each candidate is chosen with a probability equal to its performance score. The adaptive algorithm is general, being able to employ any candidate scheduling algorithm and to react to any combination of quality of service preferences. As part of this thesis, the Raindrop system was developed as exploratory test bed in which to conduct an experimental study. In that experimental study, the adaptive algorithm was shown to be effective in outperforming single scheduling algorithms for many QoS combinations and data arrival patterns.

"Video-based sensor networks can be used to improve environment surveillance, health care and emergency response. Many sensor network scenarios require multiple high quality video streams that share limited wireless bandwidth. At the same time, the lifetime of wireless video sensors are constrained by the capacity of their batteries. Media scaling may extend battery life by reducing the video data rate while still maintaining visual quality, but comes at the expense of additional compression time. This thesis studies the effects of media scaling on video sensor energy consumption by: measuring the energy consumption on the different components of the video sensor; building a energy consumption model with several adjustable parameters to analyze the performance of a video sensor; exploring the trade-offs between the video quality and the energy consumption for a video sensor; and, finally, building a working video sensor to validate the accuracy of the model. The results show that the model is an accurate representation of the power usage of an actual video sensor. In addition, media scaling is often an effective way to reduce energy consumption in a video sensor."

Distribution over the internet is destined to become a standard approach for live broadcasting of TV or events of nation-wide interest. The demand for high-quality live video with personal requirements is destined to grow exponentially over the next few years. Endsystem multicast is a desirable option for relieving the content server from bandwidth bottlenecks and computational load by allowing decentralised allocation of resources to the users and distributed service management. Network coding provides innovative solutions for a multitude of issues related to multi-user content distribution, such as the coupon-collection problem, allocation and scheduling procedure. This thesis tackles the problem of streaming scalable video on end-system multicast overlays with prioritised push-based streaming. We analyse the characteristic arising from a random coding process as a linear channel operator, and present a novel error detection and correction system for error-resilient decoding, providing one of the first practical frameworks for Joint Source-Channel-Network coding. Our system outperforms both network error correction and traditional FEC coding when performed separately. We then present a content distribution system based on endsystem multicast. Our data exchange protocol makes use of network coding as a way to collaboratively deliver data to several peers. Prioritised streaming is performed by means of hierarchical network coding and a dynamic chunk selection for optimised rate allocation based on goodput statistics at application layer. We prove, by simulated experiments, the efficient allocation of resources for adaptive video delivery. Finally we describe the implementation of our coding system. We highlighting the use rateless coding properties, discuss the application in collaborative and distributed coding systems, and provide an optimised implementation of the decoding algorithm with advanced CPU instructions. We analyse computational load and packet loss protection via lab tests and simulations, complementing the overall analysis of the video streaming system in all its components.

Master of Science / Department of Journalism and Mass Communications / Major Professor Not Listed / Based on the Uses and Gratifications (U&G) approach and Parasocial Interaction (PSI) theory, this study examined how people use live streaming platforms in China. Uniquely, it sought to understand the effect of romantic relationships on how and why people watch Host Live Shows (HLSs) and explored the relationships between Chinese audiences and live- streamers. Through an online survey, four viewing motivations were identified: Community Building, Ego-boost, Escape, and Bandwagon. Ego-boost is a relatively new motivation of media use, which means audiences watch and interact with HLSs to get compliments, self-confidence, self- validation, and ego-boosts. This study found audiences’ perceived realism and PSI were both very neutral. However, emotion projection of audiences onto streamers was observed – most viewers highly agree that streamers are their friends. Moreover, this study found the quality of interpersonal communication is affecting audiences’ HLS dependence and the degree of PSI, while the quantity of interpersonal communication might not be – the more satisfied a person is about his/her interpersonal communication, the heavier he/she depends on HLSs and the stronger his/her PSI is. As expected, the degrees of both romantic relationship status and romantic relationship satisfaction influence people’s HLS use. While compared with females, males are affected by romantic relationships more, both the status and satisfaction level. Lastly, when it comes to people’s romantic lives and social lives, HLSs are more likely to be used as alternatives to meet their unsatisfied needs from their “real partners.”

Today's popular over-the-top (OTT) video streaming services such as YouTube, Netflix and Hulu deliver video contents to viewers using adaptive bitrate (ABR) technologies. In ABR streaming, a video player running on a viewer's device adaptively changes bitrates to match given network conditions. However, providing reliable streaming is challenging. First, an ABR player may select an inappropriate bitrate during playback due to the lack of direct knowledge of access networks, frequent user mobility and rapidly changing channel conditions. Second, OTT content is delivered to viewers without any cooperation with Internet service providers (ISPs). Last, there are no appropriate tools that evaluate the performance of ABR streaming along with video quality of experience (QoE). This thesis describes how to improve the video QoE of OTT video streaming services using ABR technologies. Our analysis starts from understanding ABR heuristics. How does ABR streaming work? What factors does an ABR player consider when switching bitrates during a download? Then, we propose our solutions to improve existing ABR streaming from the perspective of network operators who deliver video content through their networks and video service providers who build ABR players running on viewers' devices. From the network operators' point of view, we propose to find a better video content server based on round trip times (RTTs) between an edge node of a wireless network and available video content servers when a viewer requests a video. The edge node can be an Internet Service Provider (ISP) router in a Wi-Fi network and a packet data network gateway (P-GW) in a 4G network. During the experiments, our solution showed better TCP performance (e.g., higher TCP throughput during playback) 146 times out of 200 experiments (73%) over Wi-Fi networks and 162 times out of 200 experiments (81%) over 3G networks. In addition, we claim that the wireless edge nodes can assist an ABR video player in selecting the best available bitrate by controlling the available bandwidth in the radio access network between a base station and a viewer's device. In our Wi-Fi testbed, the proposed solution saved up to 21% of radio bandwidth on mobile devices and enhanced the viewing experience by reducing rebufferings during playback. Last, we assert that software-defined networking (SDN) can improve video QoE by dynamically controlling routing paths of video streaming flows based on the provisioned networking information collected from SDN-enabled networking devices. Using an off-the-shelf SDN platform, we showed that our proposed solution can reduce rebufferings by 50% and provide higher bitrates during a download. From the perspective of video service providers, higher video QoE can be achieved by improving ABR heuristics implemented in an ABR player. To support this idea, we investigated the role of playout buffer size in ABR streaming and its impact on video QoE. Through our video QoE survey, we proved that a large buffer does not always outperform a small buffer, especially under rapidly varying network conditions. Based on this finding, we suggest to dynamically change the maximum buffer size in an ABR player depending on the current capacity of its playout buffer for improving the QoE of viewers. During the experiments, our proposed solution improved the viewing experience by offering 15% higher average played bitrate, 70% fewer bitrate changes and 50% shorter rebuffering duration. Our experimental results show that even small changes of ABR heuristics and new features of network systems can greatly affect video QoE. However, it is still difficult for video service providers or network operators to evaluate new ABR heuristics or network system changes due to lack of accurate QoE monitoring systems. In order to solve this issue, we have developed YouSlow ("YouTube Too Slow!? - YouSlow") as a new approach to monitoring video QoE for the analysis of ABR performance. The lightweight web browser plug-in and mobile application are designed to monitor various playback events (e.g., rebuffering duration and frequency of bitrate changes) directly from within ABR video players and calculate statistics along with video QoE. Using YouSlow, we investigate the impact of the above playback events on video abandonment: about 10% of viewers abandoned the YouTube videos when the pre-roll ads lasted for 15 seconds. Even increasing the bitrate can annoy viewers; they prefer a high starting bitrate with no bitrate changes during playback. Our regression analysis shows that bitrate changes do not affect video abandonment significantly and the abandonment rate can be estimated accurately using the rebuffering ratio and the number of rebufferings. The thesis includes four main contributions. First, we investigate today's popular OTT video streaming services (e.g., YouTube and Netflix) that use ABR streaming technologies. Second, we propose to build QoS and QoE aware video streaming that can be implemented in existing wireless networks (e.g., Wi-Fi, 3G and 4G) and in SDN-enabled networks. Third, we propose to improve current ABR heuristics by dynamically changing the playout buffer size under varying network conditions. Last, we designed and implemented a new monitoring system for measuring video QoE.

Streaming och användandet av digitala musiktjänster tar upp en allt större del av våra vardagliga liv, vilket kan komma att påverka musikens mening och betydelse för oss som musikkonsumenter.
 Syftet med denna studie är att undersöka hur tre stycken utvalda respondenter förhåller sig till musik via dagens strömmande musiktjänster, samt ifall den digitala musikkonsumtionen påverkat respondenternas musikkonsumtion och på så vis skapat några vardagliga mönster eller beteenden hos dem. 
 Studien utgår från ett antal teoretiska ramverk, som alla stärks med tidigare forskning. Studien bygger på tre stycken huvudsakliga teman; vilka är identitet, där teorier om medierade minnen och psykologiskt ägande ligger i fokus, följt av temat musikens kulturella betydelse vilken lyfter teorier om konvergens och kulturell cirkulation. Till sist innefattar studien även temat musikens påverkan, vilken i sin tur lyfter teorier om påverkan samt om postdigital sensibilitet. Studien bygger på tre stycken kvalitativa intervjuer som utförts med musikintresserade personer i åldrarna 20-30 år. Dessa intervjuer har transkriberats och analyserats via en tematisk analys, där analysen strukturerats utifrån teman med respektive underkategorier. I analysen vävs respondenternas åsikter och tankar ihop med de teorier och den tidigare forskning som lyfts i teoridelen, där kombinationen av åsikter och teori leder till fram till analysens resultat. Resultatet av analysen visar på hur respondenterna i denna undersökning har en stark relation till musiken, där musiken spelar en väsentlig roll i det vardagliga livet. Musiken agerar många gånger tidsfördriv eller bakgrundsbrus vid andra sysselsättningar, alternativt som ett sätt att påverka re- spondenternas humör eller för att öka deras handlingskraft. 
 Digitaliseringen av musik har bidragit till en ökad lättillgänglighet och mobilitet, som har påverkat respondenternas musikkonsumtion samt bidragit till att respondenterna idag upplever musiken som mer individualiserad än någonsin. Respondenterna upplever även att den digitala musikkonsumtionen har bidragit till att skapa vissa mönster, dels i samhället och dels hos respondenterna 
själva.

Ip Tak Shun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 95-101). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.1.1 --- Media Streaming --- p.1 / Chapter 1.1.2 --- Incentive Mechanism --- p.2 / Chapter 1.2 --- Cooperative and Incentive-based Proxy-and-Client Caching --- p.4 / Chapter 1.2.1 --- Cooperative Proxy-and-Client Caching --- p.4 / Chapter 1.2.2 --- Revenue-Rewarding Mechanism --- p.5 / Chapter 1.3 --- Thesis Contribution --- p.6 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Related Work --- p.9 / Chapter 2.1 --- Media Streaming --- p.9 / Chapter 2.2 --- Incentive Mechanism --- p.11 / Chapter 2.3 --- Resource Pricing --- p.14 / Chapter 3 --- Cooperative Proxy-and-Client Caching --- p.16 / Chapter 3.1 --- Overview of the COPACC System --- p.16 / Chapter 3.2 --- Optimal Cache Allocation (CAP) --- p.21 / Chapter 3.2.1 --- Single Proxy with Client Caching --- p.21 / Chapter 3.2.2 --- Multiple Proxies with Client Caching --- p.24 / Chapter 3.2.3 --- Cost Function with Suffix Multicast --- p.26 / Chapter 3.3 --- Cooperative Proxy-Client Caching Protocol --- p.28 / Chapter 3.3.1 --- Cache Allocation and Organization --- p.29 / Chapter 3.3.2 --- Cache Lookup and Retrieval --- p.30 / Chapter 3.3.3 --- Client Access and Integrity Verification --- p.30 / Chapter 3.4 --- Performance Evaluation --- p.33 / Chapter 3.4.1 --- Effectiveness of Cooperative Proxy and Client Caching --- p.34 / Chapter 3.4.2 --- Robustness --- p.37 / Chapter 3.4.3 --- Scalability and Control Overhead --- p.38 / Chapter 3.4.4 --- Sensitivity to Network Topologies --- p.40 / Chapter 4 --- Revenue-Rewarding Mechanism --- p.43 / Chapter 4.1 --- System Model --- p.44 / Chapter 4.1.1 --- System Overview --- p.44 / Chapter 4.1.2 --- System Formulation --- p.47 / Chapter 4.2 --- Resource Allocation Game --- p.50 / Chapter 4.2.1 --- Non-Cooperative Game --- p.50 / Chapter 4.2.2 --- Profit Maximizing Game --- p.52 / Chapter 4.2.3 --- Utility Maximizing Game --- p.61 / Chapter 4.3 --- Performance Evaluation --- p.74 / Chapter 4.3.1 --- Convergence --- p.76 / Chapter 4.3.2 --- Participation Incentive --- p.77 / Chapter 4.3.3 --- Cost effectiveness --- p.85 / Chapter 5 --- Conclusion --- p.87 / Chapter A --- NP-Hardness of the CAP problem --- p.90 / Chapter B --- Optimality of the Greedy Algorithm --- p.92 / Bibliography --- p.95