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    Dynamic trajectory partition optimization method based on historical trajectory data
    (Elsevier, 2024) Yu, Xiang; Zhai, Huawei; Tian, Ruijie; Guan, Yao; Polat, Kemal; Alhudhaif, Adi
    Partitioning dynamic trajectory data can improve the efficiency and accuracy of trajectory data processing, provide a foundation for trajectory data mining and analysis. However, with the continuous growth of trajectory data scales and the urgent demand for trajectory query efficiency and accuracy, partitioning methods have become crucial. The partitioning method of dynamic trajectory data faces significant challenges in terms of spatiotemporal trajectory locality, partition load balancing, and partition time. To address these challenges, we propose a method based on historical trajectory pre-partitioning, which can store data more effectively in distributed systems. We partition similar historical trajectory data to achieve preliminary partitioning of the data. In addition, we also construct a cost model to ensure that the workload of each partition is close to consistency. Extensive experiments have demonstrated the excellent partitioning efficiency and query efficiency achieved by our design compared to other partitioning methods.
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    Tinba: Incremental partitioning for efficient trajectory analytics
    (Elsevier Sci LTD, 2023) Tian, Ruijie; Zhang, Weishi; Wang, Fei; Polat, Kemal; Alenezi, Fayadh
    Applications with mobile and sensing devices have already become ubiquitous. In most of these applications, trajectory data is continuously growing to huge volumes. Existing systems for big trajectory data organize trajectories at distributed block storage systems. Systems like DITA that use block storage (e.g., 128 MB each) are more efficient for analytical queries, but they cannot incrementally maintain the partitioned data and do not support delete operations, resulting in difficulties in trajectory analytics. In this paper, we propose an incremental trajectory partitioning framework Tinba that enables distributed block storage systems to efficiently maintain optimized partitions under incremental updates of trajectories. We employ a data flushing technique to bulk ingest trajectory data for random writing in distributed file system (DFS). We recast the incremental partitioning problem as an optimal partitioning problem and prove its NP-hardness. A cost- benefit model is proposed to address the optimal partitioning problem. Moreover, Tinba supports most of the existing similarity measures to quantify the similarity between trajectories. A heuristic is developed to instantiate the Tinba framework. Comprehensive experiments on real-world and synthetic datasets demonstrate the advancements in ingestion performance and partition quality, as opposed to other trajectory partition methods.