7638849
Efficient and scalable search on scale-free P2P networks
Unstructured peer-to-peer (P2P) systems (e.g. Gnutella) are characterized by uneven distributions of node connectivity and file sharing. The existence of “hub” nodes that have a large number of connections and “generous” nodes that share many files significantly influences performance of information search over P2P file-sharing networks. In this paper, we present a novel Scalable Peer-to-Peer Search (SP2PS) method with low maintenance overhead for resource discovery in scale-free P2P networks. Different from existing search methods which employ one heuristic to direct searches, SP2PS achieves better performance by considering both of the number of shared files and the connectivity of each neighbouring node. SP2PS enables peer nodes to forward queries to the neighbours that are more likely to have the requested files and also can help in finding the requested files in the future hops. The proposed method has been simulated in different power-law networks with different forwarding degrees and distances. From our analytic and simulation results, SP2PS achieves better performance when compared to other related methods.
16471179
Small World Architecture for Peer-to-Peer Networks
Small-world phenomenon has been observed in existing peer-to-peer (P2P) networks, such as Gnutella and Freenet. Due to the similarity of P2P networks to social networks, the previous small-world model proposed by Duncan Watts can be adopted in the design of P2P networks: each node is connected to some neighbouring nodes, and a group of nodes keep a small number of long links to randomly chosen distant nodes. Unfortunately, current unstructured search algorithms have difficulty distinguishing these random long-range shortcuts. This paper presents small world architecture for P2P networks (SWAN) with a semi-structured P2P search algorithm that is used to create and find long-range shortcuts toward remote peer groups. In SWAN, not every peer node needs to be connected to remote groups, but every peer node can easily find which peer nodes have external connections to a specific peer group
8904843
Social Peer-to-Peer for Resource Discovery
For resource discovery in social networks, people can directly contact some acquaintances that have knowledge about the resources they are looking for. However, in current peer-to-peer networks, peer nodes lack capabilities similar to social networks, making it difficult to route queries efficiently. In this paper, we present a social-like system (Social-P2P) for resource discovery by mimicking human behaviours in social networks. Different from most informed search algorithms, peer nodes learn knowledge from the results of previous searches and no additional overhead is required to obtain extra information from neighbouring nodes. Unlike community-based P2P information sharing systems, we do not intend to create and maintain peer groups or communities consciously. Peer nodes with the same interests will be highly connected to each other spontaneously. Social-P2P has been simulated in a dynamic environment. From the simulation results and analysis, Social-P2P achieved better performance than current methods
5885917
Managing peer-to-peer networks with human tactics in social interactions
Abstract
Small-world phenomena have been observed in existing peer-to-peer (P2P) networks which has proved useful in the design of P2P file-sharing systems. Most studies of constructing small world behaviours on P2P are based on the concept of clustering peer nodes into groups, communities or clusters. However, managing additional multilayer topology increases maintenance overhead, especially in highly dynamic environments. In this paper, we present Social-like P2P systems (Social-P2Ps) for object discovery by self-managing P2P topology with human tactics in social networks. In Social-P2Ps, queries are routed intelligently even with limited cached knowledge and node connections. Unlike community-based P2P file-sharing systems, we do not intend to create and maintain peer groups or communities consciously. In contrast, each node connects to other peer nodes with the same interests spontaneously by the result of daily searches.

18595985
Fault-tolerant peer-to-peer search on small-world networks
This paper presents a small world architecture for P2P networks (SWAN) for content discovery in multi-group P2P systems. A semi-structured P2P algorithm of SWAN is utilized to create and find long-range shortcuts toward remote peer groups. In SWAN, not every peer node needs to be connected to remote groups, but every peer node can easily find which peer nodes have external connections to a specific peer group. From our analysis and simulation, SWAN has the advantages of both structured and unstructured P2P networks, and can achieve good performance in both stable and dynamic environments.
15021541
A Scenario-Based Architecture Evaluation Framework for Network Enabled Capability
The vision of service-oriented computing is one of loosely coupled services that create agile applications to encapsulate business objectives and processes. The potential of services to form complex systems of systems, with emergent behaviour, necessitates the need to understand how we can we develop sufficient confidence in their qualities. In this paper we argue that successful development and evolution of service oriented computing is dependent on making informed decisions at the architectural level. This poses a number of challenges concerning how to evaluate such system architectures and the measurements and metrics that are important in their assessment. This paper describes research-in-progress into the development of a scenario-based architectural evaluation framework that allows architectural-level reasoning across systems of systems in the context of Network Enabled Capability: a UK Ministry endeavour designed to achieve enhanced[military] effect through the physical networking and coherent integration of existing and future resources.
1115065
An interactive tool for fitting surfaces to volume data
MRI and CT scanners have long been used to produce threeWe begin with a volume dataset. In this example, we have a CT scan of a human prostate. There are 256 slices. The figure to the right shows one input slice, along with rectangles that outline the locations of every 10th slice. We perform linear interpolation between each slice in the input dataset, and now we can render arbitrarily oriented image planes. The figure to the right shows one such plane.
6333240
Web video topic discovery and tracking via bipartite graph reinforcement model
Automatic topic discovery and tracking on web-shared videos can greatly benefit both web service providers and end users. Most of current solutions of topic detection and tracking were done on news and cannot be directly applied on web videos, because the semantic information of web videos is much less than that of news videos. In this paper, we propose a bipartite graph model to address this issue. The bipartite graph represents the correlation between web videos and their keywords, and automatic topic discovery is achieved through two steps - coarse topic filtering and fine topic re-ranking. First, a weight-updating co-clustering algorithm is employed to filter out topic candidates at a coarse level. Then the videos on each topic are re-ranked by analyzing the link structures of the corresponding bipartite graph. After the topics are discovered, the interesting ones can also be tracked over a period of time using the same bipartite graph model. The key is to propagate the relevant scores and keywords from the videos of interests to other relevant ones through the bipartite graph links. Experimental results on real web videos from YouKu, a YouTube counterpart in China, demonstrate the effectiveness of the proposed methods. We report very promising results.
3090205
Challenges for End-to-End Quality of Service in Heterogeneous Networks
To guarantee the end-to-end QoS (E2E Quality of Service) in heterogeneous network is an important issue to provide pervasive and trusted service. Currently, the different networks are optimized for different services, but it becomes urgent to provide pervasive service across different networks. This paper gives an overview on how to design a dynamic and integrated QoS architecture under heterogeneous networks. This paper firstly analyzes the actuality of the E2E quality of service in heterogeneous networks, and then discusses the requirements of the E2E QoS of two types of heterogeneous networks, finally points out the five key issues to solve the practical problem in the current network.
17519745
Surface Reconstruction From Non-parallel Curve Networks
Building surfaces from cross-section curves has wide applications including bio-medical modeling. Previous work in this area has mostly focused on connecting simple closed curves on parallel cross-sections. Here we consider the more general problem where input data may lie on non-parallel cross-sections and consist of curve networks that represent the segmentation of the underlying object by different material or tissue types (e.g., skin, muscle, bone, etc.) on each cross-section. The desired output is a surface network that models both the exterior surface and the internal partitioning of the object. We introduce an algorithm that is capable of handling curve networks of arbitrary shape and topology on cross-section planes with arbitrary orientations. Our algorithm is simple to implement and is guaranteed to produce a closed surface network that interpolates the curve network on each cross-section. Our method is demonstrated on both synthetic and bio-medical examples.
15609476
An Online Personalized Recommendation Model Based on Bayesian Networks
It is one of an important method of using Bayesian networks in electronic commercial recommended system. But the models of Bayesian networks for describing recommended system have a problem that it could not learn online. The paper puts forward an online personalized recommended model based on Bayesian networks. The paper uses a partial ordering to represent previous structure and find posterior distributions of every node on the orders to realize online structure learning. It also uses a correctional function to revise log likelihood for online parameter learning. The experiment shows that the model can be learned online for personalized recommended system.