STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies

Autor:	Holmberg, Ted Edward, Abdelguerfi, Mahdi, Ioup, Elias
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Computer Science - Multiagent Systems
Zdroj:	2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 2920-2929
Druh dokumentu:	Working Paper
DOI:	10.1109/BigData59044.2023.10386774
Popis:	Spatiotemporal networks' observational capabilities are crucial for accurate data gathering and informed decisions across multiple sectors. This study focuses on the Spatiotemporal Ranged Observer-Observable Bipartite Network (STROOBnet), linking observational nodes (e.g., surveillance cameras) to events within defined geographical regions, enabling efficient monitoring. Using data from Real-Time Crime Camera (RTCC) systems and Calls for Service (CFS) in New Orleans, where RTCC combats rising crime amidst reduced police presence, we address the network's initial observational imbalances. Aiming for uniform observational efficacy, we propose the Proximal Recurrence approach. It outperformed traditional clustering methods like k-means and DBSCAN by offering holistic event frequency and spatial consideration, enhancing observational coverage. Comment: 10 pages, 17 figures, 2023 IEEE International Conference on Big Data (BigData)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.14388 Zobrazit plný text záznamu View this record from Arxiv