Investigation of Context Prediction Accuracy for Different Context Abstraction Levels
Investigation of Context Prediction Accuracy for Different Context Abstraction Levels
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DOI：
10.1109/tmc.2011.170
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发表时间：
2012-06
影响因子：
7.9
通讯作者：
S. Sigg;Dawud Gordon;Georg von Zengen;M. Beigl;S. Haseloff;K. David
中科院分区：
文献类型：
--
作者：
S. Sigg;Dawud Gordon;Georg von Zengen;M. Beigl;S. Haseloff;K. David
语境预测是根据观察到的语境时间序列的先前行为来推断其进程的信息的任务。预测方法可以应用于上下文处理链中的几个抽象级别。在理论分析以及实验手段中，我们证明了输入数据的性质、输出的质量以及最终用于做出预测的处理操作的流是相互关联的。对上下文预测领域的基本概念进行了全面的讨论，研究了上下文抽象程度对上下文预测场景中上下文预测精度的影响。我们开发了一组公式，将场景相关参数与上下文预测精度的概率联系起来。结果表明，理论分析的结果也可以在模拟和实验研究中得到证实。
Context prediction is the task of inferring information about the progression of an observed context time series based on its previous behaviour. Prediction methods can be applied at several abstraction levels in the context processing chain. In a theoretical analysis as well as by means of experiments we show that the nature of the input data, the quality of the output, and finally the flow of processing operations used to make a prediction, are correlated. A comprehensive discussion of basic concepts in context prediction domains and a study on the effects of the context abstraction level on the context prediction accuracy in context prediction scenarios is provided. We develop a set of formulae that link scenario-dependent parameters to a probability for the context prediction accuracy. It is demonstrated that the results achieved in our theoretical analysis can also be confirmed in simulations as well as in experimental studies.