117,111 research outputs found

    Adaptive Index Buffer

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    With rapidly increasing datasets and more dynamic workloads, adaptive partial indexing becomes an important way to keep indexing efficiently. During times of changing workloads, the query performance suffers from inefficient tables scans while the index tuning mechanism adapts the partial index. In this paper we present the Adaptive Index Buffer. The Adaptive Index Buffer reduces the cost of table scans by quickly indexing tuples in memory until the partial index has adapted to the workload again. We explain the basic operating mode of an Index Buffer and discuss how it adapts to changing workload situations. Further, we present three experiments that show the Index Buffer at work

    Perceived adaptive capacity of New Zealand dairy farmers in the face of policy and economic volatility : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Farm Management at Massey University

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    Increasing global demand for dairy products provided the New Zealand dairy sector with the opportunity to expand. However, that expansion has come at a cost, with dairy farms becoming more reliant on external feed sources, increased debt financing, and irrigation water. At the same time, they have been exposed to a turbulent global economic environment and the increasing domestic concern over the environmental impact of dairying. Consequently, dairy farmers must balance economic efficiency and environmental sustainability in the face of increasing government regulation particularly around addressing deteriorating water quality. This requires dairy farmers and their farm businesses to be resilient, demonstrating some level of buffer capacity, adaptive capacity, and/or transformability. In a changing environment, adaptive capacity is key because a system’s existing buffer capacity is unlikely to cope with such changes. In addition, it is important to avoid inadvertent transformation. Due to the nature of adaptive capacity, the difficulty lies in attempting to measure it. To address this challenge and regarding the role of the decision-maker around adaptive capacity, a shift has begun to measure perceived adaptive capacity. For this measurement, a conceptual framework is required. A combination of five capitals and a decision-making framework was chosen. This conceptual framework is considered natural, physical, financial, human, and social capital. In addition, the risk or uncertainty confronting the business and the management practices are considered in the determination of dimensions for perceived adaptive capacity. In this research, a sequential mixed method was selected. Four in-depth case studies were conducted via face-to-face interviews, focusing on the dimensions of the defined conceptual framework for perceived adaptive capacity. These interviews helped the researcher understand the New Zealand dairy farming context. In addition, the findings from the qualitative phase, alongside previous studies in New Zealand, informed the survey, disseminated to a larger sample of dairy farmers nationwide. The response rate for the survey was 51% (106 out of 209 emails sent) with usable data for analysis of 31% (65 farmers). Principal Component Analysis and Equal Weighting were utilised to calculate the score for seven dimensions for each farmer. The Analytical Hierarchy Process helped to identify the relative importance of each dimension within the framework for each farmer. Finally, the farmer’s perceptions of these dimensions and the relative importance of dimensions were used to develop an index of perceived adaptive capacity. Introducing a new framework and developing an index for perceived adaptive capacity was novel to the literature. The framework provides a lens through the various dimensions that can be used to design a tool to assess perceived adaptive capacity. Moreover, the developed index for each farmer demonstrates that farmers have unique perceptions that build their index. Therefore, classifying farmers as adaptive or less adaptive cannot be conclusive. Instead, the relative importance of different dimensions illustrates whether the individual farmer perceives a dimension as more important than any other to them. A major step toward understanding and increasing the farmer’s adaptive capacity starts from investigating their perceptions. It includes how they see the uncertainty in the environment, how they perceive their farming systems’ capitals, and how important they see the management practices to cope with ongoing changes. The index of the perceived adaptive capacity, also, assists industry agents or advisors to see the farmer’s self-assessment of their capacity to adapt to ongoing changes. In addition, the farmer’s performance in a chosen timeframe shows the consistency (or lack of) between their perceptions and actions. A gap between perceptions and actions can result in a lack of adaptive capacity and may ultimately lead to an inadvertent transformation for the business

    Towards SVC-based adaptive streaming in information centric networks

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    HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services. In HAS, each video is segmented and stored in different qualities. The client can dynamically select the most appropriate quality level to download, allowing it to adapt to varying network conditions. As the Internet was not designed to deliver such applications, optimal support for multimedia delivery is still missing. Information Centric Networking (ICN) is a recently proposed disruptive architecture that could solve this issue, where the focus is given to the content rather than to end-to-end connectivity. Due to the bandwidth unpredictability typical of ICN, standard AVC-based HAS performs quality selection sub-optimally, thus leading to a poor Quality of Experience (QoE). In this article, we propose to overcome this inefficiency by using Scalable Video Coding (SVC) instead. We individuate the main advantages of SVC-based HAS over ICN and outline, both theoretically and via simulation, the research challenges to be addressed to optimize the delivered QoE

    PERFORMANCE ANALYSIS AND PLAYOUT TIME ESTIMATION FOR MULTIMEDIA OVER INTERNET PROTOCOL (MOIP)

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    This thesis presents the algorithms to estimate the minimum buffering delay and playout delay. The possibility to use the buffering delay estimation in Multimedia Application at the receiver site will reduce the effect of jitter and will also optimize the packet loss. The increasing of Traffic of loaded generator will influence the network behaviour and affect to transmission data using video conferencing netmeeting application over the network connectio
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