分布式系统中的资源发现机制综述

资源发现问题亦即由给定的资源描述找到满足该描述的资源地址。如何快速并且准确地从分布存储的资源中找到所需的资源,是一个挑战性的问题。传统的网格资源发现系统采用注册中心和索引的方式,但这些方式不能满足网格系统规模不断扩大的需求。而对等网络是一种扩展性好的分布式系统,采用已有的P2P技术解决网格环境下的资源发现问题是一种有效的途径。介绍了网格系统中的资源发现、P2P系统的资源发现以及基于P2P的网格资源发现系统,并对它们进行了比较。     

S-粗集与数据挖掘单位圆特征

给出单向S-粗集(one direction singular rough sets)、单向S-粗集对偶(dual of one direction singular rough sets)的结构。单向S-粗集与单向S-粗集对偶是改进Z.Pawlak粗集得到的,单向S-粗集与单向S-粗集对偶具有动态特性。给出单向S-粗集、单向S-粗集对偶与Z.Pawlak粗集的关系。S-粗集具有三类形式:单向S-粗集、单向S-粗集对偶、双向S-粗集,利用单向S-粗集、单向S-粗集对偶,给出数据内挖掘、数据外挖掘概念,给出数据内挖掘的外同心圆定理、数据外挖掘的内同心圆定理,并给出其应用。S-粗集是粗集理论与应用研究的新分支。     

一种基于服务覆盖网络的业务运行平台

针对分布式业务的动态性和可扩展性需求,设计并实现了业务运行平台BEPSON（Business Execution Platform Based On Service Overlay Network）。该平台基于服务覆盖网络,采用分层架构,提出并实现了具有QoS保证的动态服务发现算法以及分布式的执行策略。最后,通过实例验证了平台的有效性和灵活性。     

MANET手机通信的资源互发现算法及其实现

针对现有的移动Ad hoc网络(MANET)资源互发现算法存在问题进行研究,提出用于MANET手机通信的移动资源互发现算法(MRDA).该算法中每个节点都是等价的,使用组播和单播相结合来发送信息,可以更合理利用带宽,解决了已有MA-NET资源互发现算法采用广播方式带来的信息泛滥,占用带宽高,节点计算能力下降等问题.MRDA算法使用分布式哈希表(DHTs)存储路由表项信息,减小了网络总开销,增强了设备计算能力.同时,针对手机内存、电力受限等特点,对报文的格式进行了设计,简化了报文结构,采用动态广告报文发送间隔,有效减少了内存占用和网络传输代价.最后,建立手机终端节点之间通信的MANET环境,利用MRDA算法实现手机终端即时通信原型.     

网络拓扑自动发现系统设计与实现

摘要：设计基于SNMP和其它协议的网络拓扑发现算法,实现三层交换机和路由器为网关的网络拓扑的快速发现,尽可能地发现完整的网络,且对网络带来的额外负栽和开销尽可能小。     

在复杂网络条件中发现基因本体的隐藏信息

近年来,本体作为语义表示被广泛的应用到众多学科中。基因本体是本体优势的一个很好的例证,并作为共享控制性词汇被应用到实践当中。但是,随着本体的规模和复杂性的迅速增加,使得本体的结构变得越来越复杂而难以理解和使用。本文主要研究发现隐藏信息,诸如复杂网络大型本体的拓扑特征和潜在的重要的术语等。通过研究说明基因本体显示出与复杂网络相同的拓扑特征,就像“smallworld”和“scale-free”。本文评价了中心化方法对于本体重要概念的识别非常合适,并且表明在所有评估的中心化测量中,Betweenness Centrality是最合适的方法。     

On discovery of soft associations with “most” fuzzy quantifier for item promotion applications

In item promotion applications, there is a strong need for tools that can help to unlock the hidden profit within each individual customer’s transaction history. Discovering association patterns based on the data mining technique is helpful for this purpose. However, the conventional association mining approach, while generating “strong” association rules, cannot detect potential profit-building opportunities that can be exposed by “soft” association rules, which recommend items with looser but significant enough associations. This paper proposes a novel mining method that automatically detects hidden profit-building opportunities through discovering soft associations among items from historical transactions. Specifically, this paper proposes a relaxation method of association mining with a new support measurement, called soft support, that can be used for mining soft association patterns expressed with the “most” fuzzy quantifier. In addition, a novel measure for validating the soft-associated rules is proposed based on the estimated possibility of a conditioned quantified fuzzy event. The new measure is shown to be effective by comparison with several existing measures. A new association mining algorithm based on modification of the FT-Tree algorithm is proposed to accommodate this new support measure. Finally, the mining algorithm is applied to several data sets to investigate its effectiveness in finding soft patterns and content recommendation.     

Conceptual equivalence for contrast mining in classification learning

Learning often occurs through comparing. In classification learning, in order to compare data groups, most existing methods compare either raw instances or learned classification rules against each other. This paper takes a different approach, namely conceptual equivalence, that is, groups are equivalent if their underlying concepts are equivalent while their instance spaces do not necessarily overlap and their rule sets do not necessarily present the same appearance. A new methodology of comparing is proposed that learns a representation of each group’s underlying concept and respectively cross-exams one group’s instances by the other group’s concept representation. The innovation is fivefold. First, it is able to quantify the degree of conceptual equivalence between two groups. Second, it is able to retrace the source of discrepancy at two levels: an abstract level of underlying concepts and a specific level of instances. Third, it applies to numeric data as well as categorical data. Fourth, it circumvents direct comparisons between (possibly a large number of) rules that demand substantial effort. Fifth, it reduces dependency on the accuracy of employed classification algorithms. Empirical evidence suggests that this new methodology is effective and yet simple to use in scenarios such as noise cleansing and concept-change learning.     

虚拟计算环境中的覆盖网技术

互联网资源的成长、自治和多样等特性给资源的有效聚合带来了巨大挑战.通过覆盖网动态组织互联网资源并支持资源的高效搜索,是虚拟计算环境中资源按需聚合的重要途径之一.文中概述了虚拟计算环境中覆盖网技术的研究进展.针对互联网资源的成长性和自治性等特点,阐述了基于Kautz图的高效覆盖网拓扑构造方法,进而给出了适用于任意正则图的通用覆盖网拓扑构造方法;针对互联网资源的多样性等特点,提出了支持分组的覆盖网拓扑构造方法;在此基础上,阐述了基于覆盖网的高效区间搜索技术,并对覆盖网拓扑的优化方法及其它复杂搜索技术进行探讨.     

Motif patterns in 2D

Motif patterns consisting of sequences of intermixed solid and don’t-care characters have been introduced and studied in connection with pattern discovery problems of computational biology and other domains. In order to alleviate the exponential growth of such motifs, notions of maximal saturation and irredundancy have been formulated, whereby more or less compact subsets of the set of all motifs can be extracted, that are capable of expressing all others by suitable combinations. In this paper, we introduce the notion of maximal irredundant motifs in a two-dimensional array and develop initial properties and a combinatorial argument that poses a linear bound on the total number of such motifs. The remainder of the paper presents approaches to the discovery of irredundant motifs both by offline and incremental algorithms.