Amazon Technologies, Inc.

Hasso-platner-institut Fuer Softwaresystemtechnik Gmbh

Hasso-plattner-institut Fur Softwaresystemtechnik Gmbh

Tim Januschowski

Jan Alexander Gasthaus

Lorenzo Stella

Andrew Edward Caldwell

Jasmeet Chhabra

Hasso Plattner

Alexander Johannes Smola

Parminder Bhatia

Sainath Chowdary Mallidi

Steffen Rochel

Jan Schaffner

Valentin Flunkert

Sebastian Schelter

Yuyang Wang

Matthias Seeger

Laurent Callot

Lifan Chen

Luyang Kong

Andrey Kan

Dean P Foster

Mohamed El Fadhel Ayed

Islam Mohamed Hatem A Atta

Joos-Hendrik Boese

Arjun Ashok

Ming Chen

Baris Kurt

Pramuditha Perera

Syama Rangapuram

Mostafa Rahmani

Techniques for performing anomaly detection are described. An exemplary method includes receiving a request to detect potential anomalies using an anomaly detection system having at least one anomaly scoring model; processing the received data using the anomaly detection system to score the data to determine when the data is potentially anomalous based on one or more thresholds; requesting feedback of at least one determined potential anomaly; receiving feedback on the least one determined potential anomaly; and adjusting at least one of one or more of thresholds used to determine potential anomalies and what is considered an anomaly without adjusting the at least one anomaly scoring model.

Anomaly detection using feedback

Placement decisions may be made to place data in a multi-tenant cache. Usage of multi-tenant cache nodes for performing access requests may be obtained. Usage prediction techniques may be applied to the usage to determine placement decisions for data amongst the multi-tenant cache nodes. Placement actions for the data amongst at the multi-tenant cache nodes may be performed according to the placement decisions.

Making decisions for placing data in a multi-tenant cache

Techniques for anomaly detection are described. An exemplary method includes receiving a request to monitor for anomalies from one or more data sources; analyzing time-series data from the one or more data sources; generating a recommendation for handling the determined anomaly, the recommendation generated by performing one or more of a root cause analysis, a heuristic analysis, and an incident similarity analysis; and reporting the anomaly and recommendation.

Systems, apparatuses, and methods for anomaly detection

Techniques for forecasting using deep factor models with random effects are described. A forecasting framework combines the strengths of both classical and neural forecasting methods in a global-local framework for forecasting multiple time series. A global model captures the common latent patterns shared by all time series, while a local model explains the variations at the individual level.

Deep factor models with random effects for forecasting

A composite time series forecasting model comprising a neural network sub-model and one or more state space sub-models corresponding to individual time series is trained. During training, output of the neural network sub-model is used to determine parameters of the state space sub-models, and a loss function is computed using the values of the time series and probabilistic values generated as output by the state space sub-models. A trained version of the composite model is stored.

Artificial intelligence system combining state space models and neural networks for time series forecasting

An input data set with a plurality of item descriptors comprising respective time series observations is identified. A routing directive indicating a predicate to be evaluated to determine whether a particular item descriptor is to be included in a training data set for a first learning algorithm is obtained. A plurality of learning algorithms are trained using training data sets derived from the input data set according to respective routing directives, and the trained algorithms are stored.

Training data routing and prediction ensembling at time series prediction system

A robust tenant placement and migration system, a process, and a respective controller for database-as-a-service environments are disclosed. Multiple tenants (T) are allocated on a set of in-memory database servers (S), such that workload changes depending on the tenants' behavior are considered, while maintaining strict service level objectives. The placement and migration are based on a migration command which, in turn, is based on an interleaved placement by interleaving tenants (T) across servers.

Robust tenant placement and migration in database-as-a-service environments

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Berlin, Germany

Tim Januschowski