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Raptor: Large Scale Processing of Big Raster + Vector Data

Author:

Samriddhi SinglaAuthors Info & Claims

SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data

Pages 2905 - 2907

https://doi.org/10.1145/3448016.3450585

Published: 18 June 2021 Publication History

Abstract

There has been an increase in the amount of spatial data in the recent years due to the advancements in remote sensing technology and the widespread use of smart phones and GPS technology. This has resulted in petabytes of satellite imagery as well as highly accurate geographical features such as city boundaries, roads, and others being made publicly available. Spatial data can generally be modeled in two representations: raster and vector. Satellite imagery is an example of raster data and is usually represented in form of multi-dimensional arrays. Vector data is represented as a set of points, lines, and polygons, and is used to represent geographical features such as regional boundaries.

The growth of geospatial data has helped in new scientific discoveries in a wide range of applications that require combining raster and vector data. However, traditional systems implement algorithms that work with either raster or vector data. This paper proposes a novel approach for the concurrent processing of raster and vector data.

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Cited By

Mantas AYfantis FBilidas DStamoulis GKondylatos SPrapas IPapoutsis IMaría Tárraga Habas JSevillano Marco ECastel FLaine CKoubarakis M(2024)Plato: A Semantic Data Cube System Using Ontology-Based Data Access TechnologiesIEEE Access10.1109/ACCESS.2024.345349412(130356-130374)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3453494
Shang ZEldawy A(2023)Viper: Interactive Exploration of Large Satellite Data✱✱Proceedings of the 18th International Symposium on Spatial and Temporal Data10.1145/3609956.3609966(141-150)Online publication date: 24-Aug-2023
https://doi.org/10.1145/3609956.3609966
Bilidas DMantas AYfantis FStamoulis GKoubarakis MKondylatos SPrapas IPapoutsis IDamiani MRenz MEldawy AKröger PNascimento M(2023)Fire Risk Management using Data Cubes, Machine Learning and OBDA systemsProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625615(1-4)Online publication date: 22-Dec-2023
https://doi.org/10.1145/3589132.3625615

Index Terms

Raptor: Large Scale Processing of Big Raster + Vector Data
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing

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Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data

June 2021

2969 pages

ISBN:9781450383431

DOI:10.1145/3448016

General Chairs:
Guoliang Li
Tsinghua University (China)
,
Zhanhuai Li
Northwestern Polytechnical University (China)
,
Program Chairs:
Stratos Idreos
Harvard University (USA)
,
Divesh Srivastava
AT&T (USA)

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2021

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Abstract

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USDA National Institute of Food and Agriculture
USDA National Institute of Food and Agriculture

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SIGMOD/PODS '21

Sponsor:

SIGMOD

SIGMOD/PODS '21: International Conference on Management of Data

June 20 - 25, 2021

Virtual Event, China

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Mantas AYfantis FBilidas DStamoulis GKondylatos SPrapas IPapoutsis IMaría Tárraga Habas JSevillano Marco ECastel FLaine CKoubarakis M(2024)Plato: A Semantic Data Cube System Using Ontology-Based Data Access TechnologiesIEEE Access10.1109/ACCESS.2024.345349412(130356-130374)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3453494
Shang ZEldawy A(2023)Viper: Interactive Exploration of Large Satellite Data✱✱Proceedings of the 18th International Symposium on Spatial and Temporal Data10.1145/3609956.3609966(141-150)Online publication date: 24-Aug-2023
https://doi.org/10.1145/3609956.3609966
Bilidas DMantas AYfantis FStamoulis GKoubarakis MKondylatos SPrapas IPapoutsis IDamiani MRenz MEldawy AKröger PNascimento M(2023)Fire Risk Management using Data Cubes, Machine Learning and OBDA systemsProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625615(1-4)Online publication date: 22-Dec-2023
https://doi.org/10.1145/3589132.3625615
Loukili YLakhrissi YAli S(2022)Geospatial Big Data Platforms: A Comprehensive ReviewZusammenfassung": Geospatial Big Data Platforms: ein umfassender ÜberblickKN - Journal of Cartography and Geographic Information10.1007/s42489-022-00121-772:4(293-308)Online publication date: 16-Sep-2022
https://doi.org/10.1007/s42489-022-00121-7

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