NEIST: a Neural-Enhanced Index for Spatio-Temporal Queries

Page view(s)
12
Checked on Dec 19, 2022
NEIST: a Neural-Enhanced Index for Spatio-Temporal Queries
Title:
NEIST: a Neural-Enhanced Index for Spatio-Temporal Queries
Other Titles:
IEEE Transactions on Knowledge and Data Engineering
Keywords:
Publication Date:
07 October 2019
Citation:
S. Wu, Z. Pang, G. Chen, Y. Gao, C. Zhao and S. Xiang, "NEIST: a Neural-Enhanced Index for Spatio-Temporal Queries," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2019.2945947.
Abstract:
Previous work on the spatial-temporal index often adopts a simple linear model to predict the future positions of moving objects, which may generate numerous errors for complex road networks and fast moving objects. In this paper, we propose NEIST, a neural-enhanced index to process spatial-temporal queries with enhanced efficiency and accuracy, by intelligently leveraging the movement patterns among moving objects. NEIST applies a Recurrent Neural Network (RNN) model to predict future positions of moving objects based on observed trajectories. To reduce the prediction overhead, a suffix-tree is further built to index trajectories with similar suffixes, and thus similar objects within a given similarity bound are grouped together to share the same prediction result. A prediction result in NEIST represents possible positions of a group of moving objects in the next t time slots. Inside each time slot, traditional linear prediction model is then adopted and a TPR-Tree is built to support spatial-temporal queries. We use Singapore taxi trajectory dataset collected over one whole month to evaluate NEIST. Compared to previous approaches, NEIST achieves a much more efficient query performance and is able to produce about 30% more accurate results.
License type:
PublisherCopyrights
Funding Info:
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore, under NRF-NSFC Joint Research Grant Call on Data Science (NRF2016NRF-NSFC001-113).
Description:
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1558-2191
Files uploaded:

File Size Format Action
tkde-2018-12-1222r1-authors-version.pdf 10.16 MB PDF Open