Flash in Action: Scalable Spatial Data Analysis Using Markov Logic Networks
Summary: Flash uses Markov Logic Networks to express spatial probabilistic graphical models as declarative rules for scalable SPGM. It blends SPGM with scalable RDBMS learning/inference and demonstrates apps: bird monitoring, safety analysis, and land-use change. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Ibrahim Sabek (University of Minnesota)
- 2. Mashaal Musleh (University of Minnesota)
- 3. Mohamed F. Mokbel (Hamad Bin Khalifa University; Qatar Computing Research Institute; University of Minnesota)
BibTeX Citation
@article{sabek_vldb19,
title = {{Flash in Action: Scalable Spatial Data Analysis Using Markov Logic Networks}},
author = {Sabek, Ibrahim and Musleh, Mashaal and Mokbel, Mohamed F.},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {12},
pages = {1834--1837},
doi = {10.14778/3352063.3352078},
url = {https://doi.org/10.14778/3352063.3352078},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,722 | Machine Learning Meets Big Spatial Data | 2019 | VLDB | 5.3769871e-05 |
| 11,061 | Large Language Models for Spatial Analysis Queries | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 579 | Incremental Knowledge Base Construction Using DeepDive | 2015 | VLDB | 0.00016217563 |
| 1,043 | Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS | 2011 | VLDB | 0.0001244565 |
| 1,986 | Scalable Probabilistic Databases with Factor Graphs and MCMC | 2010 | VLDB | 9.3519536e-05 |
| 2,990 | Knowledge Expansion over Probabilistic Knowledge Bases | 2014 | SIGMOD | 7.8883704e-05 |
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