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In memory processing of massive point clouds for multi-core systems

Authors:

Kostis Kyzirakos,

Foteini Alvanaki,

Martin KerstenAuthors Info & Claims

DaMoN '16: Proceedings of the 12th International Workshop on Data Management on New Hardware

Article No.: 7, Pages 1 - 10

https://doi.org/10.1145/2933349.2933356

Published: 26 June 2016 Publication History

Abstract

LIDAR is a popular remote sensing method used to examine the surface of the Earth. LIDAR instruments use light in the form of a pulsed laser to measure ranges (variable distances) and generate vast amounts of precise three dimensional point data describing the shape of the Earth. Processing large collections of point cloud data and combining them with auxiliary GIS data remain an open research problem.

Past research in the area of geographic information systems focused on handling large collections of complex geometric objects stored on disk and most algorithms have been designed and studied in a single-thread setting even though multi-core systems are well established. In this paper, we describe parallel alternatives of known algorithms for evaluating spatial selections over point clouds and spatial joins between point clouds and rectangle collections.

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Cited By

Bilidas DIoannidis TMamoulis NKoubarakis M(2022)Strabo 2: Distributed Management of Massive Geospatial RDF DatasetsThe Semantic Web – ISWC 202210.1007/978-3-031-19433-7_24(411-427)Online publication date: 16-Oct-2022
https://doi.org/10.1007/978-3-031-19433-7_24
Pavlovic MBastian KGildhoff HAilamaki A(2019)Dictionary Compression in Point Cloud Data ManagementACM Transactions on Spatial Algorithms and Systems10.1145/32997705:1(1-25)Online publication date: 6-Jun-2019
https://dl.acm.org/doi/10.1145/3299770
Pavlovic MBastian KGildhoff HAilamaki A(2017)Dictionary Compression in Point Cloud Data ManagementProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3139969(1-10)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3139958.3139969

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cover image ACM Other conferences

DaMoN '16: Proceedings of the 12th International Workshop on Data Management on New Hardware

June 2016

89 pages

ISBN:9781450343190

DOI:10.1145/2933349

Copyright © 2016 ACM.

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2016

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SIGMOD/PODS'16

SIGMOD/PODS'16: International Conference on Management of Data

June 26 - July 1, 2016

California, San Francisco

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Cited By

Bilidas DIoannidis TMamoulis NKoubarakis M(2022)Strabo 2: Distributed Management of Massive Geospatial RDF DatasetsThe Semantic Web – ISWC 202210.1007/978-3-031-19433-7_24(411-427)Online publication date: 16-Oct-2022
https://doi.org/10.1007/978-3-031-19433-7_24
Pavlovic MBastian KGildhoff HAilamaki A(2019)Dictionary Compression in Point Cloud Data ManagementACM Transactions on Spatial Algorithms and Systems10.1145/32997705:1(1-25)Online publication date: 6-Jun-2019
https://dl.acm.org/doi/10.1145/3299770
Pavlovic MBastian KGildhoff HAilamaki A(2017)Dictionary Compression in Point Cloud Data ManagementProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3139969(1-10)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3139958.3139969

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