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.

To effectively manage increasing knowledge graphs in various domains, a hot research topic, knowledge graph storage management, has emerged. Existing methods are classified to relational stores and native graph stores. Relational stores are able to store large-scale knowledge graphs and convenient in updating knowledge, but the query performance weakens obviously when the selectivity of a knowledge graph query is large. Native graph stores are efficient in processing complex knowledge graph queries due to its index-free adjacent property, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. However, it is challenging to determine what data to transfer from relational store to graph store at what time. To address this problem, we formulate it as a Markov Decision Process and derive a physical design tuner DOTIL based on reinforcement learning. With DOTIL, the dual-store structure is adaptive to dynamic changing workloads. Experimental results on real knowledge graphs demonstrate that our proposed dual-store structure improves query performance up to average 43.72% compared with the most commonly used relational stores.

Existing knowledge graph stores are classified to relational stores and native graph stores. Relational stores are able to store large-scale knowledge graphs and convenient in updating data, but the query performance weakens obviously when the selectivity of a knowledge graph query is large. Graph stores are efficient in processing complex knowledge graph queries, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. However, it is challenging to determine that when we transfer which data partitions from relational store to graph store. To address this problem, we derive a physical design tuner DOTIL based on reinforcement learning. Experimental results demonstrate that the dual-store structure improves query performance up to average 50.11% compared with the most commonly used relational stores.

The great amount and complex structure of graph data bring a big challenge to graph data management. However, traditional management approaches cannot tackle the challenge. Fortunately, reinforcement learning provides a new approach to solve this problem due to its automation and adaptivity in decision making. Motivated by this, we develop April, an automatic graph data management system, which performs storage structure selection, index selection, and query optimization based on reinforcement learning. The system selects storage structure, indices effectively and automatically, and optimizes the SPARQL queries efficiently. April also offers a friendly interface for users, which allows users to interact with the system in a customized mode. We demonstrate the effectiveness and efficiency of April with two graph data benchmarks.