Flash in action: Scalable spatial data analysis using Markov Logic Networks

Ibrahim Sabek, Mashaal Musleh, Mohamed F. Mokbel

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


The current explosion in spatial data raises the need for efficient spatial analysis tools to extract useful information from such data. However, existing tools are neither generic nor scalable when dealing with big spatial data. This demo presents Flash; a framework for generic and scalable spatial data analysis, with a special focus on spatial probabilistic graphical modelling (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. To show Flash effectiveness, we demonstrate three applications that use Flash in their SPGM: (1) Bird monitoring, (2) Safety analysis, and (3) Land use change tracking.

Original languageEnglish (US)
Pages (from-to)1834-1837
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
StatePublished - 2018
Event45th International Conference on Very Large Data Bases, VLDB 2019 - Los Angeles, United States
Duration: Aug 26 2017Aug 30 2017

Bibliographical note

Publisher Copyright:
© 2019 VLDB Endowment.


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