Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA
This work addresses predictive maintenance for enterprise database systems, offering a solution to improve reliability in business applications, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the problem of detecting anomalies in SAP HANA database systems using deep learning on high-dimensional Key Performance Indicators (KPIs), with experimental results confirming the effectiveness of their implemented system and models.
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochastic modeling has limitations, particularly when analyzing the data on high-dimensional Key Performance Indicators (KPIs) from a DBMS. In recent years, Deep Learning (DL) has opened new opportunities for this complex analysis. In this paper, we present two complementary DL approaches to detect anomalies in SAP HANA. A temporal learning approach is used to detect abnormal patterns based on unlabeled historical data, whereas a spatial learning approach is used to classify known anomalies based on labeled data. We implement a system in SAP HANA integrated with Google TensorFlow. The experimental results with real-world data confirm the effectiveness of the system and models.