SAP HANA

Introduction The detection of anomalies from a given time-series is usually not an easy task. The natural association with time brings many unique features to time-series that regular 1D datasets, like time-dependency(via lagging), trend, seasonality, holiday effects, etc. Because of this, traditional statististical tests or clustering-based methods for anomaly/outlier usually will fail for time-series data, […]

In a separate blog post, we have discussed the problem of outlier detection using statistical tests. Generally speaking, statistical tests are suitable for detecting outliers that have extreme values in some numerical features. However, outliers are many and varied, and not all kind of outliers can be characterized by extreme values. In many cases for […]

In datasets with multiple features, one typical type of outliers are those corresponding to extreme values in numerical features. In this blog post, we will show how to use statistical tests algorithms in Python machine learning client for SAP HANA(i.e. hana_ml) to detect such outliers. In this blog post, you will learn: Outlier detection using […]

High Availability for SAP HANA High Availability is of the utmost importance mostly for production environments. In the case of a potential failure it will ensure that the systems remain accessible and the operations will continue while the issue is being resolved. If an outage occurs, there must be a robust system in place allowing […]