Epidemic protocols proved to be very efficient solutions for supporting dynamic and complex information diffusion in highly dis- tributed computing infrastructures, like P2P environments. They are useful bricks for building and maintaining virtual network topologies, in the form of overlay networks as well as to support pervasive diffusion of information when it is injected into the network. This paper proposes a simple architecture exploiting the features of epidemic approaches to foster a collaborative percolation of information between computing nodes belonging to the network aimed at building a system that groups similar users and spread useful information among them.
They make use of heterogeneous but semantically related information sources whereas our approach does not rely on any kind of semantic interpretation. It, in principle, enables a broader exploitation of more heterogeneous data sources. Related with our proposal is Tribler @cite_12 , a P2P television recommender system. In contrast with our approach, neighbor lists can be directly filled in by the user herself using an interface. No topology or affinity property is considered. We propose a gossip system that construct and maintain in rest groups of dynamic users based on their past activities, without needing their direct intervention.
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Most current peer-to-peer (P2P) file-sharing systems treat their users as anonymous, unrelated entities, and completely disregard any social relationships between them. However, social phenomena such as friendship and the existence of communities of users with similar tastes or interests may well be exploited in such systems in order to increase their usability and performance. In this paper we present a novel social-based P2P file-sharing paradigm that exploits social phenomena by maintaining social networks and using these in content discovery, content recommendation, and downloading. Based on this paradigm's main concepts such as taste buddies and friends, we have designed and implemented the TRIBLER P2P file-sharing system as a set of extensions to BitTorrent. We present and discuss the design of TRIBLER, and we show evidence that TRIBLER enables fast content discovery and recommendation at a low additional overhead, and a significant improvement in download performance. Copyright © 2007 John Wiley & Sons, Ltd. Peer-to-peer (P2P) systems, which are realized as overlays on top of the underlying Internet routing architecture, contribute a significant portion of today's Internet traffic. While the P2P users are a good source of revenue for the Internet Service Providers (ISPs), the immense P2P traffic also poses a significant traffic engineering challenge to the ISPs. This is because P2P systems either implement their own routing in the overlay topology or may use a P2P routing underlay [1], both of which are largely independent of the Internet routing, and thus impedes the ISP's traffic engineering capabilities. On the other hand, P2P users are primarily interested in finding their desired content quickly, with good performance. But as the P2P system has no access to the underlying network, it either has to measure the path performance itself or build its overlay topology agnostic of the underlay. This situation is disadvantageous for both the ISPs and the P2P users. To overcome this, we propose and evaluate the feasibility of a solution where the ISP offers an "oracle" to the P2P users. When the P2P user supplies the oracle with a list of possible P2P neighbors, the oracle ranks them according to certain criteria, like their proximity to the user or higher bandwidth links. This can be used by the P2P user to choose appropriate neighbors, and therefore improve its performance. The ISP can use this mechanism to better manage the immense P2P traffic, e.g., to keep it inside its network, or to direct it along a desired path. The improved network utilization will also enable the ISP to provide better service to its customers. Linked Data semantic sources, in particular DBpedia, can be used to answer many user queries. PowerAqua is an open multi-ontology Question Answering (QA) system for the Semantic Web (SW). However, the emergence of Linked Data, characterized by its openness, heterogeneity and scale, introduces a new dimension to the Semantic Web scenario, in which exploiting the relevant information to extract answers for Natural Language (NL) user queries is a major challenge. In this paper we discuss the issues and lessons learned from our experience of integrating PowerAqua as a front-end for DBpedia and a subset of Linked Data sources. As such, we go one step beyond the state of the art on end-users interfaces for Linked Data by introducing mapping and fusion techniques needed to translate a user query by means of multiple sources. Our first informal experiments probe whether, in fact, it is feasible to obtain answers to user queries by composing information across semantic sources and Linked Data, even in its current form, where the strength of Linked Data is more a by-product of its size than its quality. We believe our experiences can be extrapolated to a variety of end-user applications that wish to scale, open up, exploit and re-use what possibly is the greatest wealth of data about everything in the history of Artificial Intelligence. Recently heterogeneous information network (HIN) analysis has attracted a lot of attention, and many data mining tasks have been exploited on HIN. As an important data mining task, recommender system includes a lot of object types (e.g., users, movies, actors, and interest groups in movie recommendation) and the rich relations among object types, which naturally constitute a HIN. The comprehensive information integration and rich semantic information of HIN make it promising to generate better recommendations. However, conventional HINs do not consider the attribute values on links, and the widely used meta path in HIN may fail to accurately capture semantic relations among objects, due to the existence of rating scores (usually ranging from 1 to 5) between users and items in recommender system. In this paper, we are the first to propose the weighted HIN and weighted meta path concepts to subtly depict the path semantics through distinguishing different link attribute values. Furthermore, we propose a semantic path based personalized recommendation method SemRec to predict the rating scores of users on items. Through setting meta paths, SemRec not only flexibly integrates heterogeneous information but also obtains prioritized and personalized weights representing user preferences on paths. Experiments on two real datasets illustrate that SemRec achieves better recommendation performance through flexibly integrating information with the help of weighted meta paths.