A Distributed Data Mining System Based on Multi-agent Technology

Distributed Data Mining is expected to discover preciously unknown, implicit and valuable information from massive data set inherently distributed over a network. In recent years several approaches to distributed data mining have been developed, but only a few of them make use of intelligent agents. This paper provides the reason for applying Multi-Agent Technology in Distributed Data Mining and presents a Distributed Data Mining System based on Multi-Agent Technology that deals with heterogeneity in such environment. Based on the advantages of both the CS model and agent-based model, the system is being able to address the specific concern of increasing scalability and enhancing performance.

Data Location Mechanism in Agent-Based Autonomic Storage Systems

This paper introduces agent-based methodology to build a distributed autonomic storage system infrastructure, and an effectively negotiation mechanism based on agent is applied for data location. We present Availability-based Data Allocation （ADA） algorithm as a data placement strategy to achieve high efficient utilization of storage resources by employing multiple distributed storage resources. We use Bloom filter in each storage device to track the location of data. We present the data lookup strategy that small size of read request is handled directly, and large size of read request is handled by cooperation with storage devices.The performance evaluation shows that the data location mechanism is high available and can work well for heterogeneous autonomic storage systems.

Towards a Comprehensive Security Framework of Cloud Data Storage Based on Multi Agent System Architecture

The tremendous growth of the cloud computing environments requires new architecture for security services. Cloud computing is the utilization of many servers/data centers or cloud data storages (CDSs) housed in many different locations and interconnected by high speed networks. CDS, like any other emerging technology, is experiencing growing pains. It is immature, it is fragmented and it lacks standardization. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this paper a comprehensive security framework based on Multi-Agent System (MAS) architecture for CDS to facilitate confidentiality, correctness assurance, availability and integrity of users' data in the cloud is proposed. Our security framework consists of two main layers as agent layer and CDS layer. Our propose MAS architecture includes main five types of agents: Cloud Service Provider Agent (CSPA), Cloud Data Confidentiality Agent (CDConA), Cloud Data Correctness Agent (CDCorA), Cloud Data Availability Agent (CDAA) and Cloud Data Integrity Agent (CDIA). In order to verify our proposed security framework based on MAS architecture, pilot study is conducted using a questionnaire survey. Rasch Methodology is used to analyze the pilot data. Item reliability is found to be poor and a few respondents and items are identified as misfits with distorted measurements. As a result, some problematic questions are revised and some predictably easy questions are excluded from the questionnaire. A prototype of the system is implemented using Java. To simulate the agents, oracle database packages and triggers are used to implement agent functions and oracle jobs are utilized to create agents.

Research on Product Data Management Based on Multi-agent System

PDM (product data management) is one kind of techniques based on software and database, which integrates information and process related to products. But it is not enough to perform the complication of PDM in enterprises. Then the mechanism to harmonize all kinds of information and process is needed. The paper introduces a novel approach to implement the intelligent monitor of PDM based on MAS (multi agent system). It carries out the management of information and process by MC (monitor center). The paper first puts forward the architecture of the whole system, then defines the structure of MC and its interoperation mode.

Consensus of heterogeneous multi-agent systems based on sampled data with a small sampling delay

In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay for heterogeneous multi-agent systems is proposed. Then, the algebra graph theory, the matrix method, the stability theory of linear systems, and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing heterogeneous multi-agent systems to asymptotically achieve the stationary consensus. Finally, simulations are performed to demonstrate the correctness of the theoretical results.

Distributed C-Means Algorithm for Big Data Image Segmentation on a Massively Parallel and Distributed Virtual Machine Based on Cooperative Mobile Agents

The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.

Consensus for second-order multi-agent systems with position sampled data

In this paper, the consensus problem with position sampled data for second-order multi-agent systems is investigated.The interaction topology among the agents is depicted by a directed graph. The full-order and reduced-order observers with position sampled data are proposed, by which two kinds of sampled data-based consensus protocols are constructed. With the provided sampled protocols, the consensus convergence analysis of a continuous-time multi-agent system is equivalently transformed into that of a discrete-time system. Then, by using matrix theory and a sampled control analysis method, some sufficient and necessary consensus conditions based on the coupling parameters, spectrum of the Laplacian matrix and sampling period are obtained. While the sampling period tends to zero, our established necessary and sufficient conditions are degenerated to the continuous-time protocol case, which are consistent with the existing result for the continuous-time case. Finally, the effectiveness of our established results is illustrated by a simple simulation example.

Agent-Based Data Mining Framework for the High-Dimensional Environment

An agent-based data mining framework for the high-dimensional environment is built instead of the style of classical structural programming or the object-oriented programming. The framework supports the whole process of data mining of the high-dimensional environment. Belief-desire-joint intention agents are designed to fit the characteristic of the high-dimensional environment. At the same time, the syntax, semantics and reasoning rules of the agents are given. In the data mining system of the high-dimensional environment, agents need exchange messages. The cooperation behavior mechanism is adopted to complete the communication through the three-level pattern among agents that have their own fixed roles.

Asymptotic bounded consensus tracking of double-integrator multi-agent systems with bounded-jerk target based on sampled-data without velocity measurements

This paper investigates asymptotic bounded consensus tracking（ABCT） of double-integrator multi-agent systems（MASs） with an asymptotically-unbounded-acceleration and bounded-jerk target（AUABJT） available to partial agents based on sampled-data without velocity measurements. A sampled-data consensus tracking protocol（CTP） without velocity measurements is proposed to guarantee that double-integrator MASs track an AUABJT available to only partial agents.The eigenvalue analysis method together with the augmented matrix method is used to obtain the necessary and sufficient conditions for ABCT. A numerical example is provided to illustrate the effectiveness of theoretical results.

血清miR-345、miR-138及miR-22与晚期胃癌患者化疗敏感性的相关性分析

【目的】探讨血清微小RNA-345(miR-345)、miR-138及miR-22与晚期胃癌患者化疗敏感性的相关性。【方法】100例均接受同一化疗方案治疗的晚期胃癌患者,依据疗效分为有效组(n=69)及无效组(n=31),比较两组临床病理特征及血清相关指标,绘制受试者工作特征(ROC)曲线分析血清miR-345、miR-138及miR-22预测晚期胃癌化疗效果的价值,采用多因素Logistic回归分析影响患者化疗效果的因素。【结果】Ⅲ期患者的血清miR-345相对表达量低于Ⅳ期患者,miR-138及miR-22相对表达量高于Ⅳ期患者(P<0.05);有效组miR-345低于无效组,miR-138、miR-22高于无效组(P<0.05)。经ROC曲线分析证实,血清miR-345、miR-138及miR-22能用于预测晚期胃癌的化疗效果,其敏感度与特异性分别为0.855、0.935、0.971、0.839和0.884、0.903(均P<0.05)。多因素Logistic回归分析显示,病理分期为Ⅳ期、miR-345≥14.5、miR-138≤2及miR-22≤3.12为晚期胃癌患者化疗效果不佳的危险因素(P<0.05)。【结论】血清miR-345≥14.5、miR-138≤2、miR-22≤3.12均为导致化疗效果不佳的危险因素,其可作为评估患者化疗效果的生物学标志物。

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises and sampled-data

This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore, the effects of the sampling period on the tracking performance are analysed. It turns out that from the view point of the sampling period, there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.

基于多Agent的分布式数据挖掘模型

论文分析了分布式数据挖掘的优势和所面临的问题,讨论了Agent对分布式数据挖掘性能的增强。又进一步提出了一个基于Agent的分布式数据挖掘形式模型,并结合数据挖掘方法和知识集成技术对该模型进行了深入的分析和讨论。

基于Agent的数据仓库的研究

结合可移动的软件Ａｇｅｎｔ技术，提出了基于Ａｇｅｎｔ的数据仓库．它的提出使得用户可以更加有效地管理分布的、异构的集成环境．着重讨论了基于Ａｇｅｎｔ的数据仓库的体系结构，并在此基础上讨论了一些实现中的问题．

基于Agent的三层数据仓库系统体系结构的研究

针对传统数据仓库体系结构的不足，引入了软件Agent技术以及"数据仓库基库" 的概念，提出了一种基于Agent的三层数据仓库系统体系结构，一方面使得传统数据库更加 容易融入到数据仓库系统，增强了整个系统的伸缩性；另一方面，由于Agent技术的介入， 降低了网络数据传输量，适应了移动环境等复杂信息源的要求，拓广了数据仓库的应用领域 。

基于移动Agent及RDF的网络数据挖掘系统体系结构

1 引言随着数据挖掘的研究与应用日益广泛和深入,基于Web的网络数据挖掘正受到人们的巨大关注.

一种基于Agent的数据挖掘结果模式推荐模型

结合数据挖掘模式存储和人工智能Agent技术,提出了基于智能个性化Agent的数据挖掘结果模式的推荐模型,该推荐模型能自动对用户兴趣进行分析,并向用户推荐其感兴趣而又值得关注的挖掘结果模式,有一定的实用价值。

Agent技术在Web数据仓库结构中的应用研究

针对基于Web的数据仓库体系结构存在的问题,在该体系结构中引入软件Agent技术,运用移动Agent技术来解决传统方法难以解决的一些主要问题,提出了一种基于Agent的Web数据仓库系统体系结构。文中发挥数据仓库技术和Web技术结合的优势,重点研究了Agent技术在Web服务器端的应用,在把有用的Web数据集成并入到数据仓库中这一目前的研究热点问题中,提出了基于Agent技术将HTML页面转化为XML数据源的解决方案。此外,文章分别研究了应用服务器端和数据仓库系统中的Agent技术的应用,并提出了将Agent技术引入后实现基于Web的数据仓库体系的关键技术。

一种基于移动Agent的云端(Cloud-P2P)数据复合销毁机制

云端融合计算(Cloud-P2P)融合了云计算与对等计算环境的所有节点资源,实现了最大范围的协作与资源共享。数据销毁机制是保障用户数据的安全性和可控性的重要措施之一,然而云端计算环境本身的特性也给数据的有效销毁带来了困难。针对云端数据存储系统对数据的主动销毁、定时销毁和自销毁等复合需求,提出一种基于移动Agent的数据复合销毁机制,该方法不依赖第三方,利用移动Agent技术实现对过期、废弃型数据及时、有效、灵活的销毁,并在恶意主体对数据实施攻击时主动实施防御性数据销毁,有效增强了用户数据的安全性。针对节点上数据的具体销毁,还提出一种新颖的"数据折叠"的数据覆写方法,它充分利用数据本身进行销毁,有效降低了系统的开销。

基于Agent的入侵检测系统体系结构设计

提出并深入研究了一种基于智能体技术的入侵检测系统的体系结构。该体系结构是一种混合形结构,利用基于主机和基于网络的数据源,同时使用异常检测技术和误用检测技术。该体系结构中还引入数据挖掘的思想,利用数据挖掘技术从安全审计数据中提取关键的系统特征属性,根据这些属性生成安全审计数据的分类模型用于入侵检测,使IDS自动适应复杂多变的网络环境。