Tinba: Incremental partitioning for efficient trajectory analytics
Yükleniyor...
Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Acknowledgments This work was supported by the National Key Research and Devel-opment Program of China (2020YFF0410947) and the National Natural Science Foundation of China (62103072) . Additional funding was pro-vided by the China Postdoctoral Science Foundation (2021M690502) .
Anahtar Kelimeler
Incremental Partitioning, Big Data, Trajectory Similarity, DFS, Distributed Computing, NP-Hardness
Kaynak
Advanced Engineering Informatics
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
57
Sayı
Künye
Tian, R., Zhang, W., Wang, F., Polat, K., & Alenezi, F. (2023). Tinba: Incremental partitioning for efficient trajectory analytics. Advanced Engineering Informatics, 57, 102064.
