Abstract
The rapid development of distributed technology has made it possible to store and query massive trajectory data. As a result, a variety of schemes for big trajectory data management have been proposed. However, the factor of data transmission is not considered in most of these, resulting in a certain impact on query efficiency. In view of that, we present THBase, a coprocessor-based scheme for big trajectory data management in HBase. THBase introduces a segment-based data model and a moving-object-based partition model to solve massive trajectory data storage, and exploits a hybrid local secondary index structure based on Observer coprocessor to accelerate spatiotemporal queries. Furthermore, it adopts certain maintenance strategies to ensure the colocation of relevant data. Based on these, THBase designs node-locality-based parallel query algorithms by Endpoint coprocessor to reduce the overhead caused by data transmission, thus ensuring efficient query performance. Experiments on datasets of ship trajectory show that our schemes can significantly outperform other schemes.
Highlights
In recent years, with the rapid development of mobile networks and sensor technologies, trajectory data of MO (Moving Object) has exploded, using traditional database in storing and querying the massive trajectory data cannot meet the requirements already [1]
It can be seen that as the dataset size increases, the response time of CTDM increases gradually, while THBase and SPDM do not change much. This is because CTDM can only perform pruning and query based on MO, namely, it is impossible to use time conditions to exclude irrelevant trajectory data before querying HBase, and the filter overhead after accessing HBase increases with the increase of dataset
THBase proposes a storage and partition model suitable for trajectory data management in HBase, and implements L-index structure based on Observer coprocessor to accelerate spatiotemporal queries
Summary
With the rapid development of mobile networks and sensor technologies, trajectory data of MO (Moving Object) has exploded, using traditional database in storing and querying the massive trajectory data cannot meet the requirements already [1]. Compared with spatial data schemes, the schemes for spatiotemporal data management provide support for efficient time-related query as they encode the time attributes in their indexes. On the basis of spatial or spatiotemporal data research, some schemes especially for trajectory data have been proposed, such as SPDM [10], Elite [11], TrajSpark [12], UlTraMan [13], HBSTR-tree [14] et al These schemes consider both spatiotemporal attributes and important non-spatiotemporal attributes, enabling support for trajectory-based spatiotemporal queries and non-spatiotemporal queries. We design certain maintenance strategies to ensure the colocation between Region and its corresponding index On this basis, THBase designs node-locality-based parallel query algorithms by Endpoint coprocessor to reduce the data transmission overhead when querying. We introduce an Observer coprocessor-based local indexing framework, which provides efficient support for the spatiotemporal queries and achieves the colocation for index and the indexed data.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
