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Flash: scalable spatial probabilistic graphical modeling

Published: 13 February 2020 Publication History

Abstract

The current explosion in spatial data raises the need for efficient spatial analysis tools to extract useful information from such data. Spatial probabilistic graphical modeling (SPGM) is an important class of spatial data analysis that provides efficient probabilistic graphical models for spatial data. Unfortunately, existing SPGM tools are neither generic nor scalable when dealing with big spatial data. In this work, we present Flash; a framework for generic and scalable spatial probabilistic graphical modeling (SPGM). Flash exploits Markov Logic Networks (MLN) to express SPGM as a set of declarative logical rules. In addition, it provides spatial variations of the scalable RDBMS-based learning and inference techniques of MLN to efficiently perform SPGM predictions. We have evaluated Flash, based on three real spatial analysis applications, and achieved at least two orders of magnitude speed up in learning the modeling parameters over state-of-the-art computational methods.

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Published In

cover image SIGSPATIAL Special
SIGSPATIAL Special  Volume 11, Issue 3
November 2019
37 pages
EISSN:1946-7729
DOI:10.1145/3383653
Issue’s Table of Contents
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 February 2020
Published in SIGSPATIAL Volume 11, Issue 3

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Author Tags

  1. Markov logic networks
  2. scalability
  3. spatial analysis
  4. spatial probabilistic graphical models

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