Sap Se

Karthik Mohan Mokashi

Anubhav Bhatia

Simon Lee

Patrick Brose

Lukas Carullo

Lauren McMullen

Leonard Brzezinski

Harpreet Singh

Rashmi Shetty B

Kun Bao

Martin Weiss

Bhanu Mohanty

Sanjeev Agarwal

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Failure mode analytics

Provided is a system and method for predicting leading indicators for predicting occurrence of an event at a target asset. Rather than rely on traditional manufacturer-defined leading indicators for an asset, the examples herein predict leading indicators for a target asset based on actual operating conditions at the target asset. Accordingly, unanticipated operating conditions can be considered. In one example, the method may include receiving operating data of a target resource, the operating data being associated with previous occurrences of an event at the target resource, predicting one or more leading indicators of the event at the target resource based on the received operating data, each leading indicator comprising a variable and a threshold value for the variable, and outputting information about the one or more predicted leading indicators of the target resource for display via a user interface.

Predicting leading indicators of an event

Various embodiments of systems and methods to generate web application firewall specific validation rule are described herein. Initially, a web service metadata is processed to retrieve a plurality of data parameters from the web service metadata. Next, a common validation rule is generated based on the retrieved one or more data parameters. The common validation rule is then modified to generate the web application firewall specific validation rule.

Generating web application firewall specific validation rule

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San Ramon, CA, United States of America

Karthik Mohan Mokashi