Master Thesis: AI Message Tracking Prediction B2B Service Simulation

Topic

AI Message Tracking Predictions for B2B Service Simulation

Motivation and goals

The successful processing of messages with a variety of B2B services is monitored by message tracking. In addition to the status of a message, other metadata is collected here in extensive cloud landscapes. Machine learning should predict whether a message is processed incorrectly, as well as enable simulation, planning and optimization of B2B services with relevant message tracking features. The goal of this work is to develop a prediction and simulation framework for monitoring B2B services.

Tasks

• Feature engineering, cleansing, as well as enrichment of message tracking data, as well as selected metrics from system monitoring, cloud structure data, and log data. Historical data is available for several years in a data lake
• Investigate and select approaches and ML models suitable for predicting the status of a message and, if applicable, other system characteristics with the selected features
• Development and implementation of a framework for the prediction of selected features and the regular training of ML models
• Development and implementation of a simulation system including a visualization for the processing of individual messages and selected metrics of the B2B services
• Development of criteria for the evaluation of the system and the used ML models

Location: Bretten and/or Karlsruhe

Contact Recruiting:

Daniel Iwtschenko

+49 7252 96-2224

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