Visualization-aware Time Series Min-Max Caching with Error Bound Guarantees

This paper addresses the challenges in interactive visual exploration of large multi-variate time series data. Traditional data reduction techniques may improve latency but can distort visualizations. State-of-the-art methods aimed at 100% accurate visualization often fail to maintain interactive response times or require excessive preprocessing and additional storage. We propose an in-memory adaptive caching approach, MinMaxCache, that efficiently reuses previous query results to accelerate visualization performance within accuracy constraints. MinMaxCache fetches data at adaptively determined aggregation granularities to maintain interactive response times and generate approximate visualizations with accuracy guarantees. Our results show that it is up to 10 times faster than current solutions without significant accuracy compromise.