NESTOR

We have developed the current state-of-the-art data series indexes, iSAX2+ (bulk loading), ADS+ and Dumpy (adaptive), DPiSAX and Odyssey (distributed), ParIS+ and Hercules (multi-core), SING (GPU), MESSI and Elpis (in-memory), Coconut-LSM (streaming series), ULISSE (variable-length), and ProS (progressive query answering) the first data series query workload benchmark, as well as DSStat, a toolset for data series preprocessing and visualization... Moreover, we have developed unsupervised methods for subsequence anomaly detection: NormA and Series2Graph (offline), and SAND (online). These methods exhibit state-of-the-art performance across a variety of dataset characteristics and anomaly types, without the need to learn from domain knowledge, labeled data, or datasets clean from anomalies... Our research aims to change a landscape, where database systems are used merely for storing and retrieving data, by enabling scientists to transparently use specialized query processing systems..

Primary location: Paris France