关键词: 层次分析算法, 符号化, k-d树, 多维子序列, 相似性

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relevance among them which make the method to do the similarity search for time series in the low dimensionality are no longer applicable in such situation.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