An Improved Scheme for Interest Mining Based on a Reconfiguration of the Peer-to-Peer Overlay

Tan et al. proposed a scheme to improve the quality of a file search in unstructured Peer-to-Peer systems by focusing on the similarity of interest of the participating peers. Although it certainly improves the cost/performance ratio of a simple flooding-based scheme used in conventional systems, the Tan's method has a serious drawback such that a query cannot reach a target peer if a requesting peer is not connected with the target peer through a path consisting of peers to have similar interest to the given query. In order to overcome such drawback of the Tan's method, we propose a scheme to reconfigure the underlying network in such a way that a requesting peer has a neighbor interested in the given query, before transmitting a query to its neighbors. The performance of the proposed scheme is evaluated by simulation. The result of simulation indicates that it certainly overcomes the drawback of the Tan's method

A schema-based P2P network to enable publish-subscribe for multimedia content in open hypermedia systems

Open Hypermedia Systems (OHS) aim to provide efficient dissemination, adaptation and integration of hyperlinked multimedia resources. Content available in Peer-to-Peer (P2P) networks could add significant value to OHS provided that challenges for efficient discovery and prompt delivery of rich and up-to-date content are successfully addressed. This paper proposes an architecture that enables the operation of OHS over a P2P overlay network of OHS servers based on semantic annotation of (a) peer OHS servers and of (b) multimedia resources that can be obtained through the link services of the OHS. The architecture provides efficient resource discovery. Semantic query-based subscriptions over this P2P network can enable access to up-to-date content, while caching at certain peers enables prompt delivery of multimedia content. Advanced query resolution techniques are employed to match different parts of subscription queries (subqueries). These subscriptions can be shared among different interested peers, thus increasing the efficiency of multimedia content dissemination

Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network