Abstract
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 language | English |
|---|---|
| Pages (from-to) | 1834-1837 |
| Number of pages | 4 |
| Journal | Proceedings of the VLDB Endowment |
| Volume | 12 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 1 Jan 2018 |
| Event | 45th International Conference on Very Large Data Bases, VLDB 2019 - Los Angeles, United States Duration: 26 Aug 2017 → 30 Aug 2017 |
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ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science(all)
Cite this
Flash in action : Scalable spatial data analysis using Markov Logic Networks. / Sabek, Ibrahim; Musleh, Mashaal; Mokbel, Mohamed F.
In: Proceedings of the VLDB Endowment, Vol. 12, No. 12, 01.01.2018, p. 1834-1837.Research output: Contribution to journal › Conference article
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TY - JOUR
T1 - Flash in action
T2 - Scalable spatial data analysis using Markov Logic Networks
AU - Sabek, Ibrahim
AU - Musleh, Mashaal
AU - Mokbel, Mohamed F.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
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U2 - 10.14778/3352063.3352078
DO - 10.14778/3352063.3352078
M3 - Conference article
AN - SCOPUS:85074509113
VL - 12
SP - 1834
EP - 1837
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
SN - 2150-8097
IS - 12
ER -