Sensors 2014, 14(7), 12990-13005; doi:10.3390/s140712990

A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

1email, 1,2,* email, 2email, 3email, 2email and 4email
Received: 4 February 2014; in revised form: 8 July 2014 / Accepted: 14 July 2014 / Published: 21 July 2014
(This article belongs to the Section Physical Sensors)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.
Keywords: trajectory; spatio-temporal data index; R-tree; B*-tree; cloud storage
PDF Full-text Download PDF Full-Text [995 KB, uploaded 21 July 2014 10:01 CEST]

Export to BibTeX |

MDPI and ACS Style

Ke, S.; Gong, J.; Li, S.; Zhu, Q.; Liu, X.; Zhang, Y. A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases. Sensors 2014, 14, 12990-13005.

AMA Style

Ke S, Gong J, Li S, Zhu Q, Liu X, Zhang Y. A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases. Sensors. 2014; 14(7):12990-13005.

Chicago/Turabian Style

Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting. 2014. "A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases." Sensors 14, no. 7: 12990-13005.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert