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)
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Authors
- 1. Ibrahim Sabek
- 2. Mashaal Musleh
- 3. Mohamed F. Mokbel
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,789 | Machine Learning Meets Big Spatial Data | 2019 | VLDB | 4.4509194e-05 |
| 10,835 | Large Language Models for Spatial Analysis Queries | 2025 | VLDB | 4.1945683e-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 |
|---|---|---|---|---|
| 667 | Incremental Knowledge Base Construction Using DeepDive | 2015 | VLDB | 0.00018440557 |
| 1,014 | Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS | 2011 | VLDB | 0.00014640258 |
| 2,186 | Scalable Probabilistic Databases with Factor Graphs and MCMC | 2010 | VLDB | 9.3378109e-05 |
| 3,081 | Knowledge Expansion over Probabilistic Knowledge Bases | 2014 | SIGMOD | 7.6031501e-05 |
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