Providing incentives for user contribution has been one of the primary design goals of Peer-to-Peer systems. The newly-emerged BitTorrent private communities adopt Sharing Ratio Enforcement (SRE) on top of BitTorrent&...
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Enhancing reciprocity has been one of the primary motivations for the design of incentive policies in BitTorrent-like P2P systems. Reciprocity implies that peers need to contribute their bandwidth to other peers if th...
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A Cloud may be seen as a type of flexible computing infrastructure consisting of many compute nodes, where resizable computing capacities can be provided to different customers. To fully harness the power of the Cloud...
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Constant degree peer-to-peer (P2P) system is turning into the P2P domain's promising hotspot due to constant degree digraphs having good propertis. However, it is often hard to convert a stardard constant degree d...
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Constant degree peer-to-peer (P2P) system is turning into the P2P domain's promising hotspot due to constant degree digraphs having good propertis. However, it is often hard to convert a stardard constant degree digraph to a DHT schema. Thus, most researches focus on DHT's construction and maintenance, while leaving optimization and supporting to complex query behind. Underlying topology affects upper-layers' character a lot. For constant degree P2P topologies, their inherent property makes a constant degree P2P system built using classical technique be poor in the data locality, and unfit for efficient, low-cost complex queries. Aiming at this shortage, a general-purpose construction technique towards efficient complex queries is proposed, which adds an embedding transformation layer between data layer and DHT overlay. In this way, adjacent data are stored in overlay's adjacent peers and the data locality is improved, so that the number of peers referred in complex queries can be minimized with a limited time overhead. To validate this technology, the first constant degree P2P system based on Kautz digraph FissionE is reconstructed as a typical example, which includes re-allocating of resources, query algorithm and locality maintenance strategies. Experimental results show that this construction technique can ensure data locality, reduce query cost and lead to systems' efficiency without changing the underlying DHT layer.
Peer to peer backup systems store data on "unreliable" peers that can leave the system at any moment. In this case, the only way to assure durability of the data is to add redundancy using either replication...
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When a large-scale distributed interactive simulation system is running on WAN, the sites usually disperse over a wide area in geography, which results in the simulation clock of each site is hardly to be accurately s...
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Due to the large message transmission latency in distributed Virtual Environments(DVEs) on Wide Area Net-work(WAN), the effectiveness of causality consistency control of message ordering is determined by not only caus...
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It is an important issue to preserve the consistent delivering order of messages at each site in distributed Virtual Environments(DVEs). Currently, the violations of message delivery order are inclined to happen in th...
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Strongly promoted by the leading industrial companies, cloud computing becomes increasingly popular in recent years. The growth rate of cloud computing surpasses even the most optimistic predictions. A cloud applicati...
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There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the "wo...
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ISBN:
(纸本)9781577355120
There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the "word-of- mouth" effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph, in which the semantic relatedness between terms are learned from Wikipedia. For a given topic/post, the name entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through the graph clustering algorithm. To handle topic evolution, the topic model is enriched by using Wikipedia as background knowledge. Furthermore, graph edit distance is used to measure the similarity between a topic and its posts. The proposed method is tested by using the real-world blog data. Experimental results show the advantage of the proposed method on tracking the topic in short, noisy texts.
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