Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA

TL;DR

This paper introduces two deep learning methods for anomaly detection in SAP HANA's high-dimensional KPIs, enhancing proactive maintenance through temporal and spatial analysis of real-world data.

Contribution

It presents novel deep learning approaches integrated with SAP HANA for anomaly detection, combining temporal and spatial learning techniques.

Findings

Effective anomaly detection demonstrated on real-world data

Improved proactive maintenance capabilities

Integration of DL models with SAP HANA and TensorFlow

Abstract

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.