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A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

School of Software, Jiangxi Normal University, Nanchang 330022, China
Department of Civil Engineering, Ryerson University, Toronto, Ontario M5B 2K3, Canada
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
Sensors 2014, 14(7), 12990-13005;
Received: 4 February 2014 / Revised: 8 July 2014 / Accepted: 14 July 2014 / Published: 21 July 2014
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: trajectory; spatio-temporal data index; R-tree; B*-tree; cloud storage trajectory; spatio-temporal data index; R-tree; B*-tree; cloud storage
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.

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