Traceable P2P record exchange: a database-oriented approach

In recent years, peer-to-peer (P2P) technologies are used for flexible and scalable information exchange in the Internet, but there exist problems to be solved for reliable information exchange. It is important to trace how data circulates between peers and how data modifications are performed during the circulation before reaching the destination for enhancing the reliability of exchanged information. However, such lineage tracing is not easy in current P2P networks, since data replications and modifications are performed independently by autonomous peers—this creates a lack of reliability among the records exchanged. In this paper, we propose a framework for traceable record exchange in a P2P network. By managing historical information in distributed peers, we make the modification and exchange histories of records traceable. One of the features of our work is that the database technologies are utilized for realizing the framework. Histories are maintained in a relational database in each peer, and tracing queries are written in the datalog query language and executed in a P2P network by cooperating peers. This paper describes the concept of the framework and overviews the approach to query processing.     

基于分类树的P2P文件共享机制

基于一种改进型的Chord路由模型,将层次分类技术应用到P2P结构中,设计了一种名为CTI-Chord的P2P文件共享机制。利用Chord高效定位优势,引入层次分类方法,将分类树作为模型的中心数据结构,形成新型P2P框架。用户信息发布、获取和更新不再基于关键字而是依赖于类别属性,实现了对模糊搜索的支持。树结构的可扩展性也十分有利于所构建模型的可扩展性。由于分类树具有良好的可重构性,用户可以部分下载自己所感兴趣的子树,组装成自己的个人分类树,进行个性化的共享信息定制。     

一种基于SVM的P2P网络流量分类方法

提出一种基于SVM的P2P网络流量分类的方法。这种方法利用网络流量的统计特征和基于统计理论的SVM方法,对不同应用类型的P2P网络流量进行分类研究。主要对文件共享中的BitTorrent,流媒体中的PPLive,网络电话中的Skype,即时通讯中的MSN 4种P2P网络流量进行分类研究。介绍了基于SVM的P2P流量分类的整体框架,描述了流量样本的获取及处理方法,并对分类器的构建及实验结果进行了介绍。实验结果验证了提出方法的有效性,平均分类精确率为92.38%。     

P2P-FISM: Mining (recently) frequent item sets from distributed data streams over P2P network

Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims.     

Top-$\boldsymbol{k}$ query processing over uncertain data in distributed environments

Although top-k queries over uncertain data in centralized databases have been studied widely in recent years, it is still a challenging issue in distributed environments. In distributed environments, such as Peer-to-Peer (P2P) systems and sensor networks, there exists an inherent uncertainty on the data objects due to imprecise measurements and network delays. Therefore, it is necessary to study the problem of how to efficiently retrieve top-k uncertain data objects over distributed environments with minimum network overhead. In this paper, we propose a novel approach of processing uncertain top-k queries in large-scale P2P networks, where datasets are horizontally partitioned over peers. In our approach, each peer constructs an Uncertain Quad-Tree (UQ-Tree) index for its local uncertain data, while the P2P network constructs a global index by summarizing the local indexes. Based on the global index, we propose a spatial-pruning algorithm to reduce communication costs and a distributed-pruning algorithm to reduce computation costs. Extensive experiments are conducted to verify the effectiveness and efficiency of the proposed methods in terms of communication costs and response time.     

P2P Object-based adaptivE Multimedia Streaming (POEMS)

Peer-to-peer (P2P) paradigm has recently gained tremendous attraction and is widely used for content distribution and sharing. The future multimedia communication applications have to support the user’s needs, the terminal capabilities, the content specification and the underlying networking technologies. They should be network-aware, topology-aware, and end-user-centric. Thus, in this paper, we use the characteristics of the object-based encoding scheme and P2P network topology to propose adaptive content delivery architecture for P2P networks. We propose an efficient mechanism for transmission of real-time content over P2P networks, called POEMS (P2P Object-based adaptivE Multimedia Streaming). This object-based audio-visual quality adaptive mechanism over P2P networks is media-aware, network-aware, and user-centric that is carried-out through (1) selection of appropriate sending peers willing to participate in the streaming mechanism, (2) organization of sending peers by constructing an overlay network to facilitate content delivery and adaptation, (3) dynamicity management of peers when some peer enters or leaves the system to maintain an acceptable level of perceived video quality, and (4) ensuring the end-to-end QoS (Quality of Services) by orchestrating the overall streaming mechanism. The obtained results demonstrate that combining content adaptation using object-based encoding and advance network-aware peers selection based on peer monitoring leads to intelligent, efficient, and large-scale support of multimedia services over complex network architectures.

Multidimensional hierarchical classification

Hierarchical classification can be seen as a multidimensional classification problem where the objective is to predict a class, or set of classes, according to a taxonomy. There have been different proposals for hierarchical classification, including local and global approaches. Local approaches can suffer from the inconsistency problem, that is, if a local classifier has a wrong prediction, the error propagates down the hierarchy. Global approaches tend to produce more complex models. In this paper, we propose an alternative approach inspired in multidimensional classification. It starts by building a multi-class classifier per each parent node in the hierarchy. In the classification phase, all the local classifiers are applied simultaneously to each instance, providing a probability for each class in the taxonomy. Then the probability of the subset of classes, for each path in the hierarchy, is obtained by combining the local classifiers results. The path with highest probability is returned as the result for all the levels in the hierarchy. As an extension of the proposal method, we also developed a new technique, based on information gain, to classifies at different levels in the hierarchy. The proposed method was tested on different hierarchical classification data sets and was compared against state-of-the-art methods, resulting in superior predictive performance and/or efficiency to the other approaches in all the datasets.     

Collaborative SVM classification in scale-free peer-to-peer networks

Distributed classification in large-scale P2P networks has gained relevance in recent years and support applications like distributed intrusion detection in P2P monitoring environments, online match-making, personalized information retrieval, distributed document classification in a P2P media repository and P2P recommender systems to mention a few. However, classification in a P2P network is a challenging task due to the constraints such as centralization of data is not feasible, scarce communication bandwidth, scalability, synchronization and peer dynamism. Moreover, without considering data distributions and topological scenarios of real world P2P systems, most of the existing distributed classification approaches lack in their predictive and network-cost performance. In this paper, we investigate a collaborative classification method (TRedSVM) based on Support Vector Machines (SVM) in Scale-free P2P networks. In particular, we demonstrate how to construct SVM classifier in real world P2P networks which exhibit inherently skewed distribution of node links and eventually data. The proposed method propagates the most influential instances of SVM models to the vast majority of scarcely connected peers in a controlled way that improves their local classification accuracy and, at the same time, keeps the communication cost low throughout the network. Besides using benchmark Machine Learning data sets for extensive experimental evaluations, we have evaluated the proposed method particularly for music genre classification to exhibit its performance in a real application scenario. Additionally, performance analysis is carried out with respect to centralized approaches, data replication in P2P networks and cost accuracy trade-off. TRedSVM outperforms baseline approaches of model propagation by improving the overall classification performance substantially at the cost of a tolerable increase in communication.     

A cooperative constructive method for neural networks for pattern recognition

In this paper, we propose a new constructive method, based on cooperative coevolution, for designing automatically the structure of a neural network for classification. Our approach is based on a modular construction of the neural network by means of a cooperative evolutionary process. This process benefits from the advantages of coevolutionary computation as well as the advantages of constructive methods. The proposed methodology can be easily extended to work with almost any kind of classifier.The evaluation of each module that constitutes the network is made using a multiobjective method. So, each new module can be evaluated in a comprehensive way, considering different aspects, such as performance, complexity, or degree of cooperation with the previous modules of the network. In this way, the method has the advantage of considering not only the performance of the networks, but also other features.The method is tested on 40 classification problems from the UCI machine learning repository with very good performance. The method is thoroughly compared with two other constructive methods, cascade correlation and GMDH networks, and other classification methods, namely, SVM, C4.5, and k nearest-neighbours, and an ensemble of neural networks constructed using four different methods.     

Semi-supervised ensemble classification in subspaces

Graph-based semi-supervised classification depends on a well-structured graph. However, it is difficult to construct a graph that faithfully reflects the underlying structure of data distribution, especially for data with a high dimensional representation. In this paper, we focus on graph construction and propose a novel method called semi-supervised ensemble classification in subspaces, SSEC in short. Unlike traditional methods that execute graph-based semi-supervised classification in the original space, SSEC performs semi-supervised linear classification in subspaces. More specifically, SSEC first divides the original feature space into several disjoint feature subspaces. Then, it constructs a neighborhood graph in each subspace, and trains a semi-supervised linear classifier on this graph, which will serve as the base classifier in an ensemble. Finally, SSEC combines the obtained base classifiers into an ensemble classifier using the majority-voting rule. Experimental results on facial images classification show that SSEC not only has higher classification accuracy than the competitive methods, but also can be effective in a wide range of values of input parameters.     

多层次P2P流量分类方法研究

P2P网络流量分类对网络管理和网络安全有着十分重要的意义,由于目前P2P流量多样化的发展,传统单一的P2P流量分类方法很难对其准确分类。通过分析现阶段P2P流量分类方法的现状,结合现有P2P流量分类方法的优点,提出了多层次P2P流量分类方法,该方法由四个P2P流量分类模块组成,模块间采用分工协作及反馈机制来提升P2P流量分类的效果。实验表明该方法可以有效提升P2P流量分类准确率和效率。     

基于概念漂移检测的网络数据流分类

互联网环境日新月异,使得网络数据流中存在概念漂移,对数据流的分类也由传统的静态分类变为动态分类,而如何对概念漂移进行检测是动态分类的关键。本文提出一种基于概念漂移检测的网络数据流自适应分类算法,通过比较滑动窗口中数据与历史数据的分布差异来检测概念漂移,然后将窗口中数据过采样来减少样本间的不均衡性,最后将处理后的数据集输入到OS-ELM分类器中进行在线学习,从而更新分类器使其应对数据流中的概念漂移。本文在MOA实验平台中使用合成数据集和真实数据集对提出的算法进行验证,结果表明,该算法较集成学习算法在分类准确率和稳定性上有一定的提升,并且随着数据流量的增加,时间性能上的优势开始体现,适合复杂多变的网络环境。     

Trustworthiness in P2P: performance behaviour of two fuzzy-based systems for JXTA-overlay platform

Peer-to-peer (P2P) networks, will be very important for future distributed systems and applications. In such networks, peers are heterogeneous in providing the services and they do not have the same competence of reliability. Therefore, it is necessary to estimate whether a peer is trustworthy or not for file sharing and other services. In this paper, we propose two fuzzy-based trustworthiness system for P2P communication in JXTA-overlay. System 1 has only one fuzzy logic controller (FLC) and uses four input parameters: mutually agreed behaviour (MAB), actual behaviour criterion (ABC), peer disconnections (PD) and number of uploads (NU) and the output is peer reliability (PR). System 2 has two FLCs. In FLC1 use three input parameters: number of jobs (NJ), number of connections (NC) and connection lifetime (CL) and the output is actual behavioural criterion (ABC). We use ABC and reputation (R) as input linguistic parameters for FLC2 and the output is peer reliability (PR). We evaluate the proposed systems by computer simulations. The simulation results show that the proposed systems have a good behaviour and can be used successfully to evaluate the reliability of the new peer connected in JXTA-overlay.     

Low complexity adaptive forgetting factor for online sequential extreme learning machine (OS-ELM) for application to nonstationary system estimations

Huang et al. (2004) has recently proposed an on-line sequential ELM (OS-ELM) that enables the extreme learning machine (ELM) to train data one-by-one as well as chunk-by-chunk. OS-ELM is based on recursive least squares-type algorithm that uses a constant forgetting factor. In OS-ELM, the parameters of the hidden nodes are randomly selected and the output weights are determined based on the sequentially arriving data. However, OS-ELM using a constant forgetting factor cannot provide satisfactory performance in time-varying or nonstationary environments. Therefore, we propose an algorithm for the OS-ELM with an adaptive forgetting factor that maintains good performance in time-varying or nonstationary environments. The proposed algorithm has the following advantages: (1) the proposed adaptive forgetting factor requires minimal additional complexity of O(N) where N is the number of hidden neurons, and (2) the proposed algorithm with the adaptive forgetting factor is comparable with the conventional OS-ELM with an optimal forgetting factor.     

A popularity-based query scheme in P2P networks using adaptive gossip sampling

As P2P networks, such as many forms of social networking have been rapidly growing, numerous efforts have been made to improve the efficiency of the search operation especially in terms of response time and hit ratio. To this end, popularity-based schemes have recently attracted attention aimed at increasing search efficiency using content popularity ranking; however, these methods suffer from high cost and overhead, or inappropriate level of accuracy in specifying the popularity. In this paper, we propose an adaptive sampling scheme to make a tradeoff between cost and accuracy. This scheme relies on exchanging File Index Table (FIT) between peers in a local neighborhood using a Gossip Exchange Method (GEM). The proposed Hybrid Adaptive Search According to Gossip Exchange Method (HAS-A-GEM) is based on smart unstructured peer to peer overlays. We apply a hybrid overlay that efficiently combines topology-aware and interest-based links instead of random or DHT invoked connections. An analytical model as well as a simulation framework is developed to illustrate the performance of this scheme. The effectiveness of the proposed scheme is demonstrated under various conditions. Simulation results reveal that HAS-A-GEM performs well for large-scale networks, exploiting local content popularity when each local area contains enough number of peers.     

A GroupTrust model based on service similarity evaluation in P2P networks

The open and anonymous nature of peer‐to‐peer (P2P) networks makes it an ideal medium for attackers to spread malicious contents, which in turn leads to lower quality of network services due to lack of effective trust management mechanism. To improve the quality of services (or transactions), this paper proposes a novel trust and reputation model, named as GroupTrust, based on peer group and evaluation similarity degree in P2P networks. In the proposed model, trust relationships between peers are divided into three categories: trust relationship within a peer group, trust relationship between different groups, and trust relationship between a peer in a peer group with another peer out of this peer group. The model presents the evaluation similarity degree under different context of services and gives local and global reputation computation. Experimental results demonstrate that this model can get more real trust value and deal with the malicious attacks efficiently by comparison with existing models. © 2010 Wiley Periodicals, Inc.     

P2P OLAP: Data model,implementation and case study

It is a common situation nowadays that business groups own different companies that operate in an autonomous way. Nevertheless, these companies must be requested to provide the headquarters with summarized information for decision-making. An architecture for cooperative interchange of decision-making information seems to be a natural solution for this problem. We propose the use of a peer-to-peer (P2P) architecture for addressing the problem of processing OLAP data in a distributed environment, in a way that all companies involved can maintain full autonomy over the use of its own data resources. In a scenario like this, data exchange between peers occurs when one of them, in the role of a local peer, receives a query and, for answering it, requests data available in other nodes, denoted acquaintances. No global schema is assumed to exist for any data under this computing paradigm. Henceforth, data provided by an acquaintance of a local peer must be adapted, in a manner that answers to queries posed by local peer users conform the view those users have of their data. Because multidimensional data normally consist of a collection of views of aggregated data, a careful translation process is needed in this case, in order to transform any summary concept that appears in a peer acquaintance into a summary concept meaningful to the requesting peer. We first present a model for multidimensional data distributed in a P2P network, and a query rewriting technique, that allows a local peer to propagate OLAP queries among its acquaintances, obtaining a meaningful and correct answer. Mappings are performed using a novel technique called revise and map, based on belief revision concepts. Revising a dimension instance allows to produce consistent aggregations when an OLAP query is answered at more than one node. We then describe an implementation of a P2P system for answering OLAP queries over a network of data warehouses. We apply our proposal to a real-world case study of an insurance group. Finally, we report the results of an experimental evaluation of our implementation, and discuss the issues that must be accounted for in this setting.     

Sporadic decentralized resource maintenance for P2P distributed storage networks

In this paper, we propose a novel decentralized resource maintenance strategy for peer-to-peer (P2P) distributed storage networks. Our strategy relies on the Wuala overlay network architecture, (The WUALA Project). While the latter is based, for the resource distribution among peers, on the use of erasure codes, e.g., Reed–Solomon codes, here we investigate the system behavior when a simple randomized network coding strategy is applied. We propose to replace the Wuala regular and centralized strategy for resource maintenance with a decentralized strategy, where users regenerate new fragments sporadically, namely every time a resource is retrieved. Both strategies are analyzed, analytically and through simulations, in the presence of either erasure and network coding. It will be shown that the novel sporadic maintenance strategy, when used with randomized network coding, leads to a fully decentralized solution with management complexity much lower than common centralized solutions.     

A path-traceable query routing mechanism for search in unstructured peer-to-peer networks

Unstructured Peer-to-Peer (P2P) networks have become a very popular architecture for content distribution in large-scale and dynamic environments. Searching for content in unstructured P2P networks is a challenging task because the distribution of objects has no association with the organization of peers. Proposed methods in recent years either depend too much on objects replication rate or suffer from a sharp decline in performance when objects stored in peers change rapidly, although their performance is better than flooding or random walk algorithms to some extent. In this paper, we propose a novel query routing mechanism for improving query performance in unstructured P2P networks. We design a data structure called traceable gain matrix (TGM) that records every query's gain at each peer along the query hit path, and allows for optimizing query routing decision effectively. Experimental results show that our query routing mechanism achieves relatively high query hit rate with low bandwidth consumption in different types of network topologies under static and dynamic network conditions.