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Webservices Oriented Data Mining In Knowledge Architecture

机译:知识架构中面向Web服务的数据挖掘

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Massive parallelism is required for an efficient solution to data mining tasks, considering the proliferation of data and the need for high computational effort. The DisDaMin (Distributed Data Mining) project addressed distributed discovery and knowledge discovery through parallelization of data mining tasks. DisDaMin algorithms are based on the DG-ADAJ (Desktop-Grid Adaptive Application in Java), a middleware platform for Desktop Grid used as a deployment mechanism for DisDaMin algorithms.rnOn the top of DG-ADAJ, SOA specific components could be employed to provide additional features and to improve current operation. An ESB (Enterprise Service Bus) built on the top of DG-ADAJ is going to provide improved availability, solve interoperability issues by exposing services through well established interfaces, and offer a loosely coupled infrastructure. An additional enactment layer is going to improve the performance of intelligent fragmentation of data, offering at the same time the necessary support for the execution of data workflows.
机译:考虑到数据的激增以及对大量计算工作的需求,大规模并行性是有效解决数据挖掘任务的方法所必需的。 DisDaMin(分布式数据挖掘)项目通过并行化数据挖掘任务解决了分布式发现和知识发现。 DisDaMin算法基于DG-ADAJ(Java桌面网格自适应应用程序),该桌面平台中间件平台用作DisDaMin算法的部署机制。在DG-ADAJ的顶部,可以使用SOA特定组件来提供附加功能并改善当前操作。建立在DG-ADAJ顶部的ESB(企业服务总线)将提供更高的可用性,通过建立良好的接口公开服务来解决互操作性问题,并提供松散耦合的基础结构。另一个制定层将改善数据智能分段的性能,同时为数据工作流的执行提供必要的支持。

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