.png){kind=icon}
.png){kind=icon}
Return
An efficient data processing framework for mining the massive trajectory of moving objects
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compenvurbsys.2015.03.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, there has been increasing development of positioning technology, which enables us to collect large scale trajectory data for moving objects. Efficient processing and analysis of massive trajectory data has thus become an emerging and challenging task for both researchers and practitioners. Therefore, in this paper, we propose an efficient data processing framework for mining massive trajectory data. This framework includes three modules: (1) a data distribution module, (2) a data transformation module, and (3) a high performance I/O module. Specifically, we first design a two-step consistent hashing algorithm, which takes into account load balancing, data locality, and scalability, for a data distribution module. In the data transformation module, we present a parallel strategy of a linear referencing algorithm with reduced subtask coupling, easy-implemented parallelization, and low communication cost. Moreover, we propose a compression-aware I/O module to improve the processing efficiency. Finally, we conduct a comprehensive performance evaluation on a synthetic dataset (1.114 TB) and a real world taxi GPS dataset (578 GB). The experimental results demonstrate the advantages of our proposed framework. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Big data
Trajectory of moving object
Compression contribution model
Parallel linear referencing
Two step consistent hashing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.3
Papers:
1.6K
Citations:
8.3K
Organization
View more organizations