Harnessing Big Data for Enhanced Spatial-Temporal Insights: the Role of Oryx MLLM

EasyChair Preprint 15044

Abstract

The explosion of big data has led to significant advancements in how we understand spatial and temporal dynamics across various industries. The Oryx Machine Learning Language Model (MLLM) offers a sophisticated platform for extracting actionable insights from vast datasets by focusing on spatial-temporal patterns. This article explores how Oryx MLLM leverages machine learning to enhance the analysis of spatial-temporal data, enabling better decision-making across fields such as urban planning, environmental monitoring, and supply chain optimization. The integration of big data analytics and spatial-temporal modeling promises to revolutionize industries by offering precise, real-time insights that were previously unattainable.

Keyphrases: Big Data, Data Analytics, Oryx MLLM, Spatial-Temporal Insights, decision making, machine learning

@booklet{EasyChair:15044,
  author    = {Toluwani Bolu},
  title     = {Harnessing Big Data for Enhanced Spatial-Temporal Insights: the Role of Oryx MLLM},
  howpublished = {EasyChair Preprint 15044},
  year      = {EasyChair, 2024}}