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Multi-attribute Trajectory Data Management

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Handbook of Big Geospatial Data

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Abstract

Motivated by the trend of enriching the knowledge about movement data, this chapter introduces representing and querying multi-attribute trajectories in a database system. Such a trajectory contains a sequence of time-stamped locations and a set of descriptive attributes. Multi-attribute trajectories mainly deal with attributes describing characteristics of objects, i.e., attributes that are not relevant to locations. This differs from semantic and activity trajectories in the literature that focus on location-dependent information. A range of novel queries are incurred that integrate both spatio-temporal data and attributes into the evaluation. To enhance the query performance, a hybrid and flexible index is developed to manage multi-attribute trajectories. Efficiently updating the structure is also presented. Query algorithms are proposed, accompanied with optimization strategies. Furthermore, the chapter introduces the system development, reports the performance evaluation and points out future directions.

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Notes

  1. 1.

    Given three rectangles a, b, c, each contains a set of points inside. We aim to find the nearest point to a given point inside a. Let Max(b, a) and Min(c, a) denote the maximum and minimum distances between two rectangles. If Max(b, a) ≤ Min(c, a), then no point inside the rectangle c can be closer than a point in the rectangle b to a. As a consequence, we can omit c when searching for the nearest neighbor to a.

  2. 2.

    The space is contained by the node but there are few or no data objects. One can also call this dead space (Tao and Papadias 2001), meaning that the area will be evaluated but few objects are there or even no object exists.

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Acknowledgements

This work is supported by NSFC under grants 61972198, Natural Science Foundation of Jiangsu Province of China under grants BK20191273 and National Key Research and Development Plan of China (2018YFB1003902). Thanks Weiwei Wang for developing the 3-D visualization tool and Xiangyu Wei for developing the monitoring tool.

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Jianqiu Xu

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  1. Jianqiu Xu
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Correspondence to Jianqiu Xu .

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Editors and Affiliations

  1. Professorship Big Geospatial Data Management, Technical University of Munich (TUM), Munich, Germany

    Martin Werner

  2. Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA

    Yao-Yi Chiang

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Xu, J. (2021). Multi-attribute Trajectory Data Management. In: Werner, M., Chiang, YY. (eds) Handbook of Big Geospatial Data. Springer, Cham. https://doi.org/10.1007/978-3-030-55462-0_9

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