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POIsam: A System for Efficient Selection of Large-scale Geospatial Data on Maps

conference contribution
posted on 2024-11-03, 12:36 authored by Tao Guo, Mingzhao Li, Peishan Li, Zhifeng Bao, Gao Cong
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 result w.r.t. user's zoom in, zoom out and panning operations when they interact with the map. POISam drops a common assumption from all previous work, i.e. the zoom levels and region cells are pre-defined and indexed, and objects are selected from such region cells at a particular zoom level rather than from user's current region of interest (which in most cases do not correspond to the pre-defined cells). It results in extra challenge as we need to do object selection via online computation. To our best knowledge, this is the first system that is able to meet all the four features to achieve an interactive visualization map exploration system.

Funding

Continuous and summarised search over evolving heterogeneous data

Australian Research Council

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Continuous intent tracking for virtual assistance using big contextual data

Australian Research Council

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History

Start page

1677

End page

1680

Total pages

4

Outlet

Proceedings of the 2018 International Conference on Management of Data (SIGMOD 2018)

Name of conference

SIGMOD 2018

Publisher

Association for Computing Machinery

Place published

New York, United States

Start date

2018-06-10

End date

2018-06-15

Language

English

Copyright

© 2018 Association for Computing Machinery

Former Identifier

2006088620

Esploro creation date

2020-06-22

Fedora creation date

2019-02-21

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