SEB-tree: An Approach to Index Continuously Moving Objects

Song, Zhexuan; Roussopoulos, Nick

doi:10.1007/3-540-36389-0_25

Cited by 62 publications

(26 citation statements)

References 3 publications

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“…The TPR-tree [16], TPR*-tree [18], STRIPE [14], and the B x -tree [6] focus on indexing of predicted trajectories of moving objects. Work on indexing of historical trajectory includes TB-tree [15], MVR-tree [5], SEB-tree [17], SETI [2] and PA-tree [11]. Index structures are proposed in such work to support range queries, nearest neighbor or reverse nearest neighbor queries.…”

Section: Related Workmentioning

confidence: 99%

Pointwise-Dense Region Queries in Spatio-temporal Databases

Ravishankar

2007

2007 IEEE 23rd International Conference on Data Engineering

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Section: Related Workmentioning

confidence: 99%

Pointwise-Dense Region Queries in Spatio-temporal Databases

Ravishankar

2007

2007 IEEE 23rd International Conference on Data Engineering

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“…The differences in the characteristics between the two object types are shown in Table 1. For selection and nearest neighbor queries for objects in O c , one can use an index structure such as TB-tree [15] or SEB-tree [16].…”

Section: Problem Definitionmentioning

confidence: 99%

A Framework for Moving Sensor Data Query and Retrieval of Dynamic Atmospheric Events

Tang

Liu

et al. 2010

Lecture Notes in Computer Science

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Abstract. One challenge in Earth science research is the accurate and efficient ad-hoc query and retrieval of Earth science satellite sensor data based on user-defined criteria to study and analyze atmospheric events such as tropical cyclones. The problem can be formulated as a spatiotemporal join query to identify the spatio-temporal location where moving sensor objects and dynamic atmospheric event objects intersect, either precisely or within a user-defined proximity. In this paper, we describe an efficient query and retrieval framework to handle the problem of identifying the spatio-temporal intersecting positions for satellite sensor data retrieval. We demonstrate the effectiveness of our proposed framework using sensor measurements from QuikSCAT (wind field measurement) and TRMM (precipitation vertical profile measurements) satellites, and the trajectories of the tropical cyclones occurring in the North Atlantic Ocean in 2009.

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“…Song and Roussopoulos proposed SEB-trees, based on a zone-based update policy [29]. Their method, like SETI, divides space into nonoverlapping zones.…”

Section: Related Workmentioning

confidence: 99%

Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations

Ravishankar

2007

IEEE Trans. Knowl. Data Eng.

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Abstract-Complex queries on trajectory data are increasingly common in applications involving moving objects. MBR or grid-cell approximations on trajectories perform suboptimally since they do not capture the smoothness and lack of internal area of trajectories. We describe a parametric space indexing method for historical trajectory data, approximating a sequence of movement functions with single continuous polynomial. Our approach works well, yielding much finer approximation quality than MBRs. We present the PA-tree, a parametric index that uses this method, and show through extensive experiments that PA-trees have excellent performance for offline and online spatio-temporal range queries. Compared to MVR-trees, PA-trees are an order of magnitude faster to construct and incur I/O cost for spatio-temporal range queries lower by a factor of 2-4. SETI is faster than our method for index construction and timestamp queries, but incurs twice the I/O cost for time interval queries, which are much more expensive and are the bottleneck in online processing. Therefore, the PA-tree is an excellent choice for both offline and online processing of historical trajectories.

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SEB-tree: An Approach to Index Continuously Moving Objects

Cited by 62 publications

References 3 publications

Pointwise-Dense Region Queries in Spatio-temporal Databases

Pointwise-Dense Region Queries in Spatio-temporal Databases

A Framework for Moving Sensor Data Query and Retrieval of Dynamic Atmospheric Events

Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations

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