Widespread use of IoT devices that is predicted to generate 79 zettabytes of data annually by 2025 is only one example to emphasize the importance of time series data mining, especially anomaly detection both in academia and industry. Despite extensive research producing hundreds of algorithms, current frameworks inadequately abstract technical complexity for domain experts. In this dissertation, we introduce a novel framework that efficiently aggregates and summarizes anomaly scores from very high-dimensional datasets comprising millions of data points, enabling flexible query support and precise responses. Our approach employs a windowing technique to transform multidimensional anomaly scores into a transaction database, thereby leveraging established itemset mining algorithms.