Tiling strategies for distributed point cloud databases

János M. Szalai-Gindl, László Dobos, I. Csabai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many large point clouds-such as cosmological N-body simulations, intersections of road networks etc.-are strongly clustered on a hierarchy of scales. In shared nothing distributed environments, optimized tiling of data is crucial to minimize cross-server communication and balance IO and processing load. We propose histogram-based tiling algorithms, a hierarchical tiling and a spectral clustering algorithm, that can be incorporated into the data extraction or transformation phase of a typical Extraction-Transformation-Loading (ETL) procedure. We dehne measures to characterize the performance of these tiling techniques with respect to typical spatial search operations, and evaluate the algorithms based on these measures using hierarchically clustered data sets.

Original languageEnglish
Title of host publicationSSDBM 2017
Subtitle of host publication29th International Conference on Scientific and Statistical Database Management
PublisherAssociation for Computing Machinery
VolumePart F128636
ISBN (Electronic)9781450352826
DOIs
Publication statusPublished - Jun 27 2017
Event29th International Conference on Scientific and Statistical Database Management, SSDBM 2017 - Chicago, United States
Duration: Jun 27 2017Jun 29 2017

Other

Other29th International Conference on Scientific and Statistical Database Management, SSDBM 2017
CountryUnited States
CityChicago
Period6/27/176/29/17

Keywords

  • ETL process
  • Hierarchical tiling
  • Multi-dimensional histogram partitioning
  • Scientific databases
  • Spectral clustering

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Szalai-Gindl, J. M., Dobos, L., & Csabai, I. (2017). Tiling strategies for distributed point cloud databases. In SSDBM 2017: 29th International Conference on Scientific and Statistical Database Management (Vol. Part F128636). [a32] Association for Computing Machinery. https://doi.org/10.1145/3085504.3085537