Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case

Abstract

Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is nontrivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose Sem-Cloud, a semantics-enhanced cloud system, that couples cloud systemwith semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelises the semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing noncloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.