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Problem-solving Environment (problem-solving + environment)
Selected AbstractsCompetence Models and the Maintenance ProblemCOMPUTATIONAL INTELLIGENCE, Issue 2 2001Barry Smyth Case-based reasoning (CBR) systems solve problems by retrieving and adapting the solutions to similar problems that have been stored previously as a case base of individual problem solving episodes or cases. The maintenance problem refers to the problem of how to optimize the performance of a CBR system during its operational lifetime. It can have a significant impact on all the knowledge sources associated with a system (the case base, the similarity knowledge, the adaptation knowledge, etc.), and over time, any one, or more, of these knowledge sources may need to be adapted to better fit the current problem-solving environment. For example, many maintenance solutions focus on the maintenance of case knowledge by adding, deleting, or editing cases. This has lead to a renewed interest in the issue of case competence, since many maintenance solutions must ensure that system competence is not adversely affected by the maintenance process. In fact, we argue that ultimately any generic maintenance solution must explicitly incorporate competence factors into its maintenance policies. For this reason, in our work we have focused on developing explanatory and predictive models of case competence that can provide a sound foundation for future maintenance solutions. In this article we provide a comprehensive survey of this research, and we show how these models have been used to develop a number of innovative and successful maintenance solutions to a variety of different maintenance problems. [source] A Grid-enabled problem-solving environment for advanced reservoir uncertainty analysisCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 18 2008Zhou Lei Abstract Uncertainty analysis is critical for conducting reservoir performance prediction. However, it is challenging because it relies on (1) massive modeling-related, geographically distributed, terabyte, or even petabyte scale data sets (geoscience and engineering data), (2) needs to rapidly perform hundreds or thousands of flow simulations, being identical runs with different models calculating the impacts of various uncertainty factors, (3) an integrated, secure, and easy-to-use problem-solving toolkit to assist uncertainty analysis. We leverage Grid computing technologies to address these challenges. We design and implement an integrated problem-solving environment ResGrid to effectively improve reservoir uncertainty analysis. The ResGrid consists of data management, execution management, and a Grid portal. Data Grid tools, such as metadata, replica, and transfer services, are used to meet massive size and geographically distributed characteristics of data sets. Workflow, task farming, and resource allocation are used to support large-scale computation. A Grid portal integrates the data management and the computation solution into a unified easy-to-use interface, enabling reservoir engineers to specify uncertainty factors of interest and perform large-scale reservoir studies through a web browser. The ResGrid has been used in petroleum engineering. Copyright © 2008 John Wiley & Sons, Ltd. [source] Programming scientific and distributed workflow with Triana servicesCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 10 2006David Churches Abstract In this paper, we discuss a real-world application scenario that uses three distinct types of workflow within the Triana problem-solving environment: serial scientific workflow for the data processing of gravitational wave signals; job submission workflows that execute Triana services on a testbed; and monitoring workflows that examine and modify the behaviour of the executing application. We briefly describe the Triana distribution mechanisms and the underlying architectures that we can support. Our middleware independent abstraction layer, called the Grid Application Prototype (GAP), enables us to advertise, discover and communicate with Web and peer-to-peer (P2P) services. We show how gravitational wave search algorithms have been implemented to distribute both the search computation and data across the European GridLab testbed, using a combination of Web services, Globus interaction and P2P infrastructures. Copyright © 2005 John Wiley & Sons, Ltd. [source] Enabling interactive and collaborative oil reservoir simulations on the GridCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 11 2005Manish Parashar Abstract Grid-enabled infrastructures and problem-solving environments can significantly increase the scale, cost-effectiveness and utility of scientific simulations, enabling highly accurate simulations that provide in-depth insight into complex phenomena. This paper presents a prototype of such an environment, i.e. an interactive and collaborative problem-solving environment for the formulation, development, deployment and management of oil reservoir and environmental flow simulations in computational Grid environments. The project builds on three independent research efforts: (1) the IPARS oil reservoir and environmental flow simulation framework; (2) the NetSolve Grid engine; and (3) the Discover Grid-based computational collaboratory. Its primary objective is to demonstrate the advantages of an integrated simulation infrastructure towards effectively supporting scientific investigation on the Grid, and to investigate the components and capabilities of such an infrastructure. Copyright © 2005 John Wiley & Sons, Ltd. [source] Distributed computing with Triana on the GridCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 9 2005Ian Taylor Abstract In this paper, we describe Triana, a distributed problem-solving environment that makes use of the Grid to enable a user to compose applications from a set of components, select resources on which the composed application can be distributed and then execute the application on those resources. We describe Triana's current pluggable architecture that can support many different modes of operation by the use of flexible writers for many popular Web service choreography languages. We further show, that the Triana architecture is middleware-independent through the use of the Grid Application Toolkit (GAT) API and demonstrate this through the use of a GAT binding to JXTA. We describe how other bindings being developed to Grid infrastructures, such as OGSA, can seamlessly be integrated within the current prototype by using the switching capability of the GAT. Finally, we outline an experiment we conducted using this prototype and discuss its current status. Copyright © 2005 John Wiley & Sons, Ltd. [source] Enabling interactive and collaborative oil reservoir simulations on the GridCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 11 2005Manish Parashar Abstract Grid-enabled infrastructures and problem-solving environments can significantly increase the scale, cost-effectiveness and utility of scientific simulations, enabling highly accurate simulations that provide in-depth insight into complex phenomena. This paper presents a prototype of such an environment, i.e. an interactive and collaborative problem-solving environment for the formulation, development, deployment and management of oil reservoir and environmental flow simulations in computational Grid environments. The project builds on three independent research efforts: (1) the IPARS oil reservoir and environmental flow simulation framework; (2) the NetSolve Grid engine; and (3) the Discover Grid-based computational collaboratory. Its primary objective is to demonstrate the advantages of an integrated simulation infrastructure towards effectively supporting scientific investigation on the Grid, and to investigate the components and capabilities of such an infrastructure. Copyright © 2005 John Wiley & Sons, Ltd. [source] Component-based, problem-solving environments for large-scale scientific computingCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 13-15 2002Chris Johnson Abstract In this paper we discuss three scientific computing problem solving environments: SCIRun, BioPSE, and Uintah. We begin with an overview of the systems, describe their underlying software architectures, discuss implementation issues, and give examples of their use in computational science and engineering applications. We conclude by discussing future research and development plans for the three problem solving environments. Copyright © 2002 John Wiley & Sons, Ltd. [source] |