DITA: Distributed In-Memory Trajectory Analytics

Shang, Z, Li, G and Bao, Z 2018, 'DITA: Distributed In-Memory Trajectory Analytics', in Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, United States, 10-15 June 2018, pp. 725-740.

Document type: Conference Paper
Collection: Conference Papers

Title DITA: Distributed In-Memory Trajectory Analytics
Author(s) Shang, Z
Li, G
Bao, Z
Year 2018
Conference name 2018 International Conference on Management of Data (SIGMOD 2018)
Conference location Houston, United States
Conference dates 10-15 June 2018
Proceedings title Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018
Publisher ACM
Place of publication United States
Start page 725
End page 740
Total pages 16
Abstract Trajectory analytics can benefit many real-world applications, e.g., frequent trajectory based navigation systems, road planning, car pooling, and transportation optimizations. Existing algorithms focus on optimizing this problem in a single machine. However, the amount of trajectories exceeds the storage and processing capability of a single machine, and it calls for large-scale trajectory analytics in distributed environments. The distributed trajectory analytics faces challenges of data locality aware partitioning, load balance, easy-to-use interface, and versatility to support various trajectory similarity functions. To address these challenges, we propose a distributed in-memory trajectory analytics system DITA. We propose an effective partitioning method, global index and local index, to address the data locality problem. We devise cost-based techniques to balance the workload. We develop a filter-verification framework to improve the performance. Moreover, DITA can support most of existing similarity functions to quantify the similarity between trajectories. We integrate our framework seamlessly into Spark SQL, and make it support SQL and DataFrame API interfaces. We have conducted extensive experiments on real world datasets, and experimental results show that DITA outperforms existing distributed trajectory similarity search and join approaches significantly.
Subjects Database Management
DOI - identifier 10.1145/3183713.3183743
Copyright notice © 2018 Association for Computing Machinery
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