Abstract: High dimensionality of QAR and the uncertain relevance among them which make the method to do the similarity search for time series in the low dimensionality are no longer applicable in such situation. Taking into account the specificity of the civil aviation industry, with the similarity search for QAR to ascertain the plane faults requires a special definition of the similarity. In this paper, expertise and analytic hierarchy process algorithm are combined to be used to calculate the weightiness of different dimensionalities for the plane fault. It translates the QAR data with the symbolic method, and then builds a k-d tree index, which makes it possible to do the similarity search on multidimensional QAR data subsequences. Shape and distance are used toghther to define similarity. The high precision and the low cost are proved by the experiments in this paper.

Key words: analytic hierarchy process, symbolic, k-d tree, multidimensional subsequence, similarity

摘要： QAR数据的高维度以及维度之间不确定的相互关联性，使得原有低维空间上度量时间序列的相似性的方法不再适用，另一方面由于民航行业的特殊性，利用QAR数据进行相似性搜索来确定飞行故障，对相似性的定义也有特殊的要求。通过专家经验结合一种层次分析算法来确定飞行故障所关联的属性维度的重要性，对QAR数据的多维子序列进行符号化表示，并利用k-d树的特殊性质建立索引，使QAR数据多维子序列的快速相似性搜索成为可能，结合形状和距离对相似性进行定义和度量，实验证明查找速度快，准确度较为满意。

关键词: 层次分析算法, 符号化, k-d树, 多维子序列, 相似性