Conference Proceedings

POIsam: A system for efficient selection of large-scale geospatial data on maps

T Guo, M Li, P Li, Z Bao, G Cong

Proceedings of the ACM SIGMOD International Conference on Management of Data | ASSOC COMPUTING MACHINERY | Published : 2018

Abstract

In this demonstration we present POIsam, a visualization system supporting the following desirable features: representativeness, visibility constraint, zooming consistency, and panning consistency. The first two constraints aim to efficiently select a small set of representative objects from the current region of user's interest, and any two selected objects should not be too close to each other for users to distinguish in the limited space of a screen. One unique feature of POISam is that any similarity metrics can be plugged into POISam to meet the user's specific needs in different scenarios. The latter two consistencies are fundamental challenges to efficiently update the selection resul..

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University of Melbourne Researchers

Grants

Awarded by MOE Tier-2 grant


Awarded by MOE Tier-1 grant


Awarded by ARC


Awarded by NSFC


Funding Acknowledgements

This work was supported by the MOE Tier-2 grant MOE2016-T2-1-137, MOE Tier-1 grant RG31/17 and a grant from Microsoft. This work was also supported by the ARC DP180102050, ARC DP170102726, NSFC 61728204, and NSFC 91646204. Zhifeng Bao is a recipient of Google Faculty Research Award.