TY - GEN
T1 - Similarity Measurement of Trajectory Data Stream Based on Incremental DBSCAN
AU - Fan, Yue
AU - Wang, Huiwen
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the rapid development of mobile internet and devices, a large amount of trajectory data of moving objects has been generated. The similarity measurement of trajectory data plays a crucial role in exploring valuable information, such as human mobile behavior and patterns. However, most existing work constructs similarity measurement offline, which cannot provide timely feedback for time sensitive applications. In light of this, we present a novel hierarchical framework based on incremental DBSCAN in this paper to measure the similarity between users in real-time. Our proposal includes a hierarchical directed graph framework (HDGF) to ensure the accuracy of trajectory similarity measurement and a stream processing algorithm based on incremental DBSCAN (IDBSCAN) to update the hierarchical framework of users in real-time. Experiment results on real trajectory dataset show that our proposed method is effective and offers competitive performance compared with related methods.
AB - With the rapid development of mobile internet and devices, a large amount of trajectory data of moving objects has been generated. The similarity measurement of trajectory data plays a crucial role in exploring valuable information, such as human mobile behavior and patterns. However, most existing work constructs similarity measurement offline, which cannot provide timely feedback for time sensitive applications. In light of this, we present a novel hierarchical framework based on incremental DBSCAN in this paper to measure the similarity between users in real-time. Our proposal includes a hierarchical directed graph framework (HDGF) to ensure the accuracy of trajectory similarity measurement and a stream processing algorithm based on incremental DBSCAN (IDBSCAN) to update the hierarchical framework of users in real-time. Experiment results on real trajectory dataset show that our proposed method is effective and offers competitive performance compared with related methods.
KW - data stream processing
KW - hierarchical directed graph
KW - incremental DBSCAN
KW - trajectory similarity measurement
UR - https://www.scopus.com/pages/publications/85190690118
U2 - 10.1109/ACDP59959.2023.00044
DO - 10.1109/ACDP59959.2023.00044
M3 - 会议稿件
AN - SCOPUS:85190690118
T3 - Proceedings - 2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023
SP - 230
EP - 235
BT - Proceedings - 2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023
Y2 - 23 June 2023 through 25 June 2023
ER -