DITA: A Distributed In-Memory Trajectory Analytics System

Shang, Z, Li, G and Bao, Z 2018, 'DITA: A Distributed In-Memory Trajectory Analytics System', in Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, United States, 10-15 June 2018, pp. 1681-1684.

Document type: Conference Paper
Collection: Conference Papers

Title DITA: A Distributed In-Memory Trajectory Analytics System
Author(s) Shang, Z
Li, G
Bao, Z
Year 2018
Conference name SIGMOD Conference 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 New York, NY, USA
Start page 1681
End page 1684
Total pages 4
Abstract Trajectory analytics can benefit many real-world applications, e.g., frequent trajectory based navigation systems, road planning, car pooling, and transportation optimizations. In this paper, we demonstrate a distributed in-memory trajectory analytics system DITA to support large-scale trajectory data analytics. DITA exhibit three unique features. First, DITA supports threshold-based and KNN-based trajectory similarity search and join operations, as well as range queries (i.e., space and time). Second, DITA is versatile to support most existing similarity functions to cater for different analytic purposes and scenarios. Last, DITA is seamlessly integrated into Spark SQL to support easy-to-use SQL and DataFrame API interfaces. Technically, DITA proposes an effective partitioning method, global index and local index, to address the data locality problem. It also devises cost-based techniques to balance the workload, and develops a filter-verification framework for efficient and scalable search and join.
Subjects Database Management
DOI - identifier 10.1145/3183713.3183743
Copyright notice © 2018 Association for Computing Machinery
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