Predictive Big Data for Sustainable AgriTech and Hospitality
Keywords:
Scalable Big Data Processing, Predictive Decision Systems, Sustainable AgriTech, Smart Hospitality Analytics, High-Throughput Data Pipelines, Real-Time Predictive Analytics, Demand Forecasting Models, Resource Optimization Systems, AI-Driven Irrigation, Weather-Driven Analytics, Multi-Timescale Forecasting, Operational Cost Optimization, Data-Driven Decision Making, Environmental Impact Analytics, Smart Tourism Systems, Smart City Integration, Mobility Data Analytics, Capacity Planning Models, Hospitality Resource Allocation, Lifecycle Analytics.Abstract
Scalable big data processing and predictive decision systems for sustainable AgriTech and smart hospitality are built on core big data principles: maximizing processing throughput while minimizing completion time and resource consumption. Underlying decision systems support real-time predictions, long-term forecasting, and model evaluation, delivering predictive analytics tailored to both smart hospitality and sustainable AgriTech contexts. In AgriTech, artificial intelligence models for predictive irrigation rely on a data-driven architecture that integrates large-scale data from public repositories with real-time weather feeds. In hospitality, a multi-timescale approach supports resource and operational cost management through demand forecasting and optimal capacity allocation. Together, these applications are framed within an integrated life-cycle perspective that accounts for both environmental and operational impact.
The growing volume and variety of data generated within hospitality operations further drives the need for robust predictive analytics. Predictive systems informed by environmental and mobility data play a key role in advancing Smart Tourism and are increasingly embedded within broader Smart City ecosystems. Capacity and flow constraints inherent to hospitality environments give rise to decision-system use cases centered on demand forecasting and resource allocation. Ultimately, data-driven decision systems forecast demand streams and provide the architectural support needed to manage hospitality processes efficiently.
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Data Availability Statement
No new datasets were generated. The analysis draws on publicly available repositories, open weather and remote-sensing sources, and public APIs cited in the paper. No additional raw data are available.
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Copyright (c) 2023 Anumandla Mukesh (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
