Quality-driven resource-adaptive data stream mining?
Quality-driven resource-adaptive data stream mining?
复制标题
质量驱动的资源自适应数据流挖掘？
DOI：
10.1145/2031331.2031342
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Michael Gertz
中科院分区：
文献类型：
--
作者：
Conny Junghans;Marcel Karnstedt;Michael Gertz
近年来，数据流变得无处不在，并在各种平台上处理，从专用高端服务器到电池供电的移动传感器。因此，数据流处理需要在几乎任何动态资源约束下工作。很少有流挖掘算法能够适应给定的约束，并且没有一种方法反映从资源适应到结果输出质量。在本文中，我们提出了一个通用模型，以实现动态设置中流挖掘算法的资源和质量感知。通过将影响参数和质量度量分类为多目标优化问题的组成部分，赋予其普遍适用性。以CluStream算法为例，验证了该模型的实用性。
Data streams have become ubiquitous in recent years and are handled on a variety of platforms, ranging from dedicated high-end servers to battery-powered mobile sensors. Data stream processing is therefore required to work under virtually any dynamic resource constraints. Few approaches exist for stream mining algorithms that are capable to adapt to given constraints, and none of them reflects from the resource adaptation to the resulting output quality. In this paper, we propose a general model to achieve resource and quality awareness for stream mining algorithms in dynamic setups. The general applicability is granted by classifying influencing parameters and quality measures as components of a multiobjective optimization problem. By the use of CluStream as an example algorithm, we demonstrate the practicability of the proposed model.