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Abstract Animation is a commonly used technique in information visualization for smooth transitions between different views. When observing animations of moving objects, people often need to track several specific objects while identify the major trend of movement simultaneously. In this paper, we propose a novel focus+context grouping technique to facilitate target tracking and trend identification. It divides objects into several groups based on a comprehensive tree cut algorithm and generates a staggering animation in which groups are animated sequentially. A balance between efficiency and accuracy is achieved for an effective animation planning. To evaluate the effectiveness of the proposed technique, a carefully designed user study is conducted. The results indicate that focus+context grouping is effective for users to track targets without losing context (i.e., major trend of movement). Based on the study, we discuss advantages and limitations of the proposed grouping technique and conclude with design implications. [ABSTRACT FROM AUTHOR]

Abstract: In a hybrid peer-to-peer (P2P) system, flooding and DHT are both employed for content locating. The decision to use flooding or DHT largely depends on the population of desired data. Previous works either use local information only, or do not consider dynamic factors of P2P systems. In this paper, we propose a Popularity Adaptive Search method for Hybrid (PASH) protocol. By dynamically estimating the content popularity, PASH properly selects search methods so as to efficiently saves query traffic cost and response time. We evaluate PASH through synthetic and trace-driven simulations. The results show that PASH outperforms existing approaches and it also scales well. [Copyright &y& Elsevier]