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

Published: 27 May 2018 Publication History

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 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.

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T. Guo, K. Feng, G. Cong, and Z. Bao. Efficient selection of geospatial data on maps for interactive and visualized exploration. In ACM SIGMOD, 2018.
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Cited By

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  • (2022)SAFEProceedings of the VLDB Endowment10.14778/3494124.349413515:3(513-526)Online publication date: 4-Feb-2022
  • (2021)Fast augmentation algorithms for network kernel density visualizationProceedings of the VLDB Endowment10.14778/3461535.346154014:9(1503-1516)Online publication date: 22-Oct-2021
  • (2020)QUAD: Quadratic-Bound-based Kernel Density VisualizationProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3380561(35-50)Online publication date: 11-Jun-2020

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  1. POIsam: a System for Efficient Selection of Large-scale Geospatial Data on Maps

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      cover image ACM Conferences
      SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data
      May 2018
      1874 pages
      ISBN:9781450347037
      DOI:10.1145/3183713
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 27 May 2018

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      Author Tags

      1. geospatial data visualization
      2. map exploration
      3. sampling

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      • Research-article

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      • ARC DP180102050
      • MOE2016-T2- 1-137
      • NSFC 91646204
      • RG31/17
      • NSFC 61728204
      • ARC DP170102726

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      SIGMOD/PODS '18
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      SIGMOD '18 Paper Acceptance Rate 90 of 461 submissions, 20%;
      Overall Acceptance Rate 785 of 4,003 submissions, 20%

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      Cited By

      View all
      • (2022)SAFEProceedings of the VLDB Endowment10.14778/3494124.349413515:3(513-526)Online publication date: 4-Feb-2022
      • (2021)Fast augmentation algorithms for network kernel density visualizationProceedings of the VLDB Endowment10.14778/3461535.346154014:9(1503-1516)Online publication date: 22-Oct-2021
      • (2020)QUAD: Quadratic-Bound-based Kernel Density VisualizationProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3380561(35-50)Online publication date: 11-Jun-2020

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