Self-Supervised Learning for Demand Forecasting Using Sparse and Noisy Social Media Sentiment Data

Rekha Singh

doi:10.55544/jrasb.1.2.11

Authors

Rekha Singh Independent Researcher, USA.

DOI:

https://doi.org/10.55544/jrasb.1.2.11

Keywords:

Distributed Systems, Monitoring, Metrics, Instrumentation, Statistical Analysis, Machine Learning, Scalability, Real-Time Monitoring

Abstract

This research work focuses on the foundational principles of SLO monitoring, architectural considerations for high-volume data processing systems, and advanced techniques for implementing and scaling SLO monitoring solutions. The research includes areas like metric selection, instrumentation techniques, data collection strategies, statistical analysis, and emerging trends in the field. It is a synthesis of current literature and industry practices that presents an organized guide for organizations that want to implement robust SLO monitoring in their data processing infrastructure.

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