Real Time Weather Data Analytics
Author(s)
Abstract
Abstract — Weather data analytics systems deal with immense amounts of spatiotemporal data provided by satellites, land-based systems, and web-based weather systems. But most of this data is redundant, noisy, or un- actionable. Human analysis of uninterrupted weather data is not feasible and raises the need for automated analytics systems. This paper introduces the Concept-Aware Real- Time Weather Data Analytics System designed to analyze and filter high- grade meteorological data. The proposed model combines temporal trends analysis, anomaly detection analysis, statistical feature identification, and semantic categorization of important meteorological events like temperature anomalies, rainfall extremes, storms, and abnormal pressure changes. A relevance scoring module prioritizes the input data points based on relevance weighting, while redundancy suppression is used to optimize data storage and representation.
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