Geolog: Scalable Logic Programming on Spatial Data

Grubenmann, Tobias; Lehmann, Jens

Abstract

Spatial data is ubiquitous in our data-driven society. The Logic Programming community has been investigating the use of spatial data in different settings. Despite the success of this research, the Geographic Information System (GIS) community has rarely made use of these new approaches. This has mainly two reasons. First, there is a lack of tools that tightly integrate logical reasoning into state-of-the-art GIS software. Second, the scalability of solutions has often not been tested and hence, some solutions might work on toy examples but do not scale well to real-world settings. The two main contributions of this paper are (1) the Relation Based Programming paradigm, expressing rules on relations instead of individual entities, and (2) Geolog, a tool for spatio-logical reasoning that can be installed on top of ArcMap, which is an industry standard GIS. We evaluate our new Relation Based Programming paradigm in four real-world scenarios and show that up to two orders of magnitude in performance gain can be achieved compared to the prevalent Entity Based Programming paradigm.

Citation

Grubenmann, T., & Lehmann, J. (2021, September). Geolog: Scalable Logic Programming on Spatial Data. Presented at 37th International Conference on Logic Programming, Online

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 37th International Conference on Logic Programming
• Start Date: Sep 20, 2021
• Acceptance Date: Jun 1, 2021
• Online Publication Date: Sep 17, 2021
• Publication Date: Sep 17, 2021
• Deposit Date: Jun 8, 2023
• Publicly Available Date: Jun 9, 2023
• Volume: 345
• Pages: 191-204
• Book Title: Proceedings ICLP 2021
• DOI: https://doi.org/10.4204/eptcs.345.34
• Keywords: General Earth and Planetary Sciences; General Engineering; General Environmental Science