The high sampling frequency of highway structural health monitoring systems brings a heavy burden on data storage. However, existing dynamic response identification approaches can guarantee either reduced data volume after identification or high accuracy of dynamic response identification. Motivated by this, we propose a real-time dynamic response identification method to filter meaningless data. Our method not only selects effective features from highway structural health monitoring data, but also designs a training data generation strategy for machine learning models within the dynamic response identification framework. Experimental results on real highway structural health monitoring data demonstrate that our proposed approach spends 0.4 ms to process the monitoring data generated in 1 s and saves around 91.63% storage space. Also, the recall value of our method achieves 0.91 on average.

Abstract In the process of responding to user applications on the highway structural health monitoring data sharing platforms, the objective is to decrease the network transmission costs and avoid a mass of redundant Input/Output operations between the storage server and local hard disks. Since none of existing job scheduling and structural health monitoring data analysis research has focused on this topic, we study the problem of cost-saving response scheduling for highway structural health monitoring data applications. To solve this problem, we develop a greedy response scheduler with (1+ $$\frac{1}{e-1})$$ -approximation ratio. Evaluation results demonstrate the effectiveness and efficiency of our proposed solution.

The subsequence matching problem utilizing dynamic time warping as the similarity measurement has been recognized as a key operation in time series analysis for more than two decades. Existing index-free algorithms depend on DTW lower bounds to discard the unpromising candidate. However, these approaches typically cost O ( m ) time for each candidate, where m is the length of the query. Consequently, the overhead of computing the DTW lower bounds occupies a significant portion of the time in subsequence matching tasks. This paper proposes new algorithms capable of computing the DTW lower bounds in average O (log m ) time for each candidate, substantially alleviating this bottleneck of the subsequence matching problem. In addition, this paper designs novel DTW lower bounds according to the characteristics of the subsequence matching problem, which is more effective without introducing significant computational overhead. Based on the above improvements, an efficient subsequence matching algorithm called FSMDTW is designed. Experiments conducted on both real and synthetic datasets show that the proposed algorithm is about 2.6 times faster than SOTA on short and medium-length queries and up to one order of magnitude faster on longer queries.

Motif discovery is a critical operation for analyzing series data in many applications. Recent works demonstrate the importance of finding motifs with Dynamic Time Warping. However, existing algorithms spend most of their time in computing lower bounds of Dynamic Time Warping to filter out the unpromising candidates. Specifically, the time complexity for computing these lower bounds is$O(L)$for each pair of subsequences, where$L$is the length of the motif (subsequences). This paper proposes two new lower bounds, called$LB_{f}$and$LB_{M}$, both of them only cost amortized$O(1)$time for each pair of subsequences. On real datasets, the proposed lower bounds are at least one magnitude faster than the state-of-the-art lower bounds used in motif discovery while still keeping satisfying effectiveness. Based on these faster lower bounds, this paper designs an efficient motif discovery algorithm that significantly reduces the cost of lower bounds. The experiments conducted on real datasets show the proposed algorithm is 5.6 times faster than the state-of-the-art algorithms on average.

Subsequence matching is an important and fundamental problem on time series data. This paper studies the inherent time complexity of the subsequence matching problem and designs a more efficient algorithm for solving the problem. Firstly, it is proved that the subsequence matching problem is incomputable in time O ( n 1-δ ) even allowing polynomial time preprocessing if the hypothesis SETH is true, where n is the size of the input time series and 0 ≤ δ < 1, i.e., the inherent complexity of the subsequence matching problem is ω ( n 1-δ ). Secondly, an efficient algorithm for subsequence matching problem is proposed. In order to improve the efficiency of the algorithm, we design a new summarization method as well as a novel index for series data. The proposed algorithm supports both Euclidean Distance and DTW distance with or without z -normalization. Experimental results show that the proposed algorithm is up to about 3 ~ 10 times faster than the state of art algorithm on the constrained z -normalized Euclidean Distance and DTW distance, and is up to 7 ~ 12 times faster on Euclidean Distance.