Hybrid Statistical Modeling for Anomaly Detection in Multi-Key Stores Based on Access Patterns

Tiberiu Boros, Marius Barbulescu

Abstract: Anomaly detection in datasets with massive amounts of sparse data is not a trivial task, given that working with high intake data in real-time requires careful design of the algorithms and data structures. We present a hybrid statistical modeling strategy which combines an effective data structure with a neural network for Gaussian Process Modeling. The network is trained in a residual learning fashion, which enables learning with less parameters and in fewer steps.