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CBR Approach FOR efficient execution of Workflows: Applied to Weather Data

This project explores the integration of Case-Based Reasoning (CBR) for more efficient workflow execution in weather data prediction systems. By applying CBR techniques to current weather data formats, our method addresses unique challenges such as spatial data complexity, rapid event arrivals, and similarity checking. We analyze the trade-offs between filter sizes to optimize prediction performance while overcoming the limitations of traditional rule-based systems. Ultimately, our approach aims to enhance the reliability and efficiency of weather forecasting workflows.

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CBR Approach FOR efficient execution of Workflows: Applied to Weather Data

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  1. Eran Chinthaka & Jaliya Ekanayake Class Project for: Knowledge Based Computing (B552) CBR Approach FOR efficient execution of Workflows: Applied to Weather Data

  2. Current System Rule Engine 1 2 R 3 4 Workflows Predictions

  3. Our Approach CBR 1 2 Window of Events Cases 3 4 Workflows Predictions

  4. Challenges • Weather events are in special formats. • Mostly spatial data • Similarity checking can be difficult • Events arrive at higher rates • 145 events each 4xx KB for every 5 mins • Pre-filtering • Window size • Small -> Higher performance cost • Large -> Slower prediction • Need to out perform a rule based system.

  5. Thank You!

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