Scalable Evaluation of Trajectory Queries over Imprecise Location Data
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Authors
- Xike Xie from Aalborg University
- Man Lung Yiu from Hong Kong Polytechnic University
- Reynold Cheng from the University of Hong Kong.
Motivation
This paper focus on a branch of P-Query, to query the 1 or N POIs for the given trajectory.
The challenge they tackled is such a senario that the measured locations of objects are imprecise such as flying birds to airlines and moving iceberg to shiplines.
In previous work, the model or methods were taken are too simple at some extent which results to low quality and inefficiency. So a more complex model is proposed in this paper.
Methods
Methods in this paper covers 4 chapters, and I try to understand it thoroughly by repeatedly reading but I failed again and again. I have to put it aside because of the time constraints.
The imprecise problem is simplified by considering all points on a straight segment have the same distance to one point outside. Then, the remain challenges are how to find the turning points of a trajectory and how to do the pruning in the filter-and-refinement paradigm.
The paper takes the u-bisector model to solve its problems. The geometric properties of u-bisector are studied very carefully, to make the algorithm efficiency.
Experiments
- Database: trajecories are generated by Brinkoff’s network-based mobile data generator(http://iapg.jade-hs.de/personen/brinkhoff/generator/) . And four real datasets of geographical objects in Germany and US(http://www.rtreeportal.org/)
- Query result quality metric: 1) Quality: Error function based on the Jaccard Distance to measure the similarity between the ground true result and the return result of the algorithm. 2) Efficiency: counting the clock time.
- Algorithms in comparison: 5 algs, the latter one is just put one more technique on the former one, like filter or refine, any algs from other papers are not seen. It is no doubt obviously that the more technique used, the better performance the alg has, doesn’t it? Would this be lack of sinceriy? I’m confused………….
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