157,659 research outputs found
DIAMOnDS - DIstributed Agents for MObile & Dynamic Services
Distributed Services Architecture with support for mobile agents between
services, offer significantly improved communication and computational
flexibility. The uses of agents allow execution of complex operations that
involve large amounts of data to be processed effectively using distributed
resources. The prototype system Distributed Agents for Mobile and Dynamic
Services (DIAMOnDS), allows a service to send agents on its behalf, to other
services, to perform data manipulation and processing. Agents have been
implemented as mobile services that are discovered using the Jini Lookup
mechanism and used by other services for task management and communication.
Agents provide proxies for interaction with other services as well as specific
GUI to monitor and control the agent activity. Thus agents acting on behalf of
one service cooperate with other services to carry out a job, providing
inter-operation of loosely coupled services in a semi-autonomous way. Remote
file system access functionality has been incorporated by the agent framework
and allows services to dynamically share and browse the file system resources
of hosts, running the services. Generic database access functionality has been
implemented in the mobile agent framework that allows performing complex data
mining and processing operations efficiently in distributed system. A basic
data searching agent is also implemented that performs a query based search in
a file system. The testing of the framework was carried out on WAN by moving
Connectivity Test agents between AgentStations in CERN, Switzerland and NUST,
Pakistan.Comment: 7 pages, 4 figures, CHEP03, La Jolla, California, March 24-28, 200
Distributed data mining in grid computing environments
The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper
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Distributed Data Mining: The JAM system architecture
This paper describes the system architecture of JAM (Java Agents for Meta-learning), a distributed data mining system that scales up to large and physically separated data sets. An earlyversion of the JAM system was described in Stolfo-et-al-97-KDD-JAM. Since then, JAM has evolved both architecturally and functionally and here we present the final design and implementation details of this system architecture. JAM is an extensible agent-based distributed data mining system that supports the remote dispatch and exchange of agents among participating datasites and employs meta-learning techniques to combine the multiple models that are learned. One of JAM's target applications is fraud and intrusion detection in financial information systems. A brief description of this learning task and JAM's applicability and summary results are also discussed
An investigation into the issues of multi-agent data mining
Multi-agent systems (MAS) often deal with complex applications that require distributedproblem solving. In many applications the individual and collective behaviourof the agents depends on the observed data from distributed sources. The field of DistributedData Mining (DDM) deals with these challenges in analyzing distributed dataand offers many algorithmic solutions to perform different data analysis and miningoperations in a fundamentally distributed manner that pays careful attention to the resourceconstraints. Since multi-agent systems are often distributed and agents haveproactive and reactive features, combining DM with MAS for data intensive applicationsis therefore appealing.This Chapter discusses a number of research issues concerned with the use ofMulti-Agent Systems for Data Mining (MADM), also known as agent-driven datamining. The Chapter also examines the issues affecting the design and implementationof a generic and extendible agent-based data mining framework. An ExtendibleMulti-Agent Data mining System (EMADS) Framework for integrating distributeddata sources is presented. This framework achieves high-availability and highperformance without compromising the data integrity and security. © 2010 Nova Science Publishers, Inc. All rights reserved
An agent-based service oriented architecture for risk mining
University of Technology, Sydney. Faculty of Engineering and Information Technology.Risk Mining (RM) is the process of analyzing data including risk information by data mining methods, with the mining results for risk prevention. In the last few years, some researchers have proposed the combination of data mining and agent technology (agent mining) to improve the performance of data mining methodology in the heterogeneous business environments. However, problems exist for further research with the application of risk mining systems in real industry environments to enhance the robustness of system architect, dynamic business process and model accuracy etc.
Therefore, in this thesis we present an Agent-based Service-oriented Risk Mining Architecture (ABSORM), which has been designed to facilitate the development of agent mining systems to address the above issues. This thesis focuses on developing the following strategies:
• The integration of agent technology with web service.
In this framework, we propose a new and easier method, by which the system functions are not integrated into the structure of the agents, rather modeled as distributed services and applications which are invoked by the agents acting as controllers and coordinators. Therefore, techniques developed in this framework can improve the interoperability between different modules, distribution of resources, and the lack of dependency of programming languages.
• The integration of agent technology with business process management.
In this work, we develop the autonomous agents that can collaborate in a business flow, which not only increases the reusability of the system, but also eases the system development in terms of re-usability of the computational resources. A group of agents solves problems in the following way: each individual agent solves the problem individually, and then interacts with each other to finalize a business process.
• The integration of agent technology with ensemble learning methods.
In this thesis, we are interested in developing agent-based ensemble learning strategies for risk mining: each ensemble agent individually gathers the evidence about model evaluation, and then ensembles learning methods like bagging and boosting is used to obtain prediction from the individually gathered evidence. Agent based ensemble learning can provide a critical boost to risk mining where predictive accuracy is more vital than model interpretability.
The proposed architecture has been evaluated for building an online banking fraud detection system and a student risk management system. These two applications have been proved to be a sophisticated, yet user friendly, risk analysis and management tool. They are modular, interactive, dynamic and globally oriented
Multiagent System for Image Mining
The overdone growth, wide availability, and demands for remote sensing databases combined with human limits to analyze such huge datasets lead to a need to investigate tools, techniques, methodologies, and theories capable of assisting humans at extracting knowledge. Image mining arises as a solution to extract implicit knowledge intelligently and semiautomatically or other patterns not explicitly stored in the huge image databases. However, spatial databases are among the ones with the fastest growth due to the volume of spatial information produced many times a day, demanding the investigation of other means for knowledge extraction. Multiagent systems are composed of multiple computing elements known as agents that interact to pursuit their goals. Agents have been used to explore information in the distributed, open, large, and heterogeneous platforms. Agent mining is a potential technology that studies ways of interaction and integration between data mining and agents. This area brought advances to the technologies involved such as theories, methodologies, and solutions to solve relevant issues more precisely, accurately and faster. AgentGeo is evidence of this, a multiagent system of satellite image mining that, promotes advances in the state of the art of agent mining, since it relevant functions to extract knowledge from spatial databases
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