NodeGroup

Abstract

Organizations such as GE are heavily invested in applying advanced data-intensive machine learning (ML) techniques towards continually improving the performance of complex physical assets and industrial operations. This paper highlights some unique industrial data management challenges and demonstrates the need for and benefits of a knowledge-driven approach to data management that complements existing efforts by the ML systems community. Specifically, we present a novel software abstraction called a NodeGroup for accessing (within some domain-specific context) heterogeneous data so that the development of end-to-end ML-driven applications is further streamlined. We present our preliminary use of NodeGroups for ML applications within a prototype (additive) manufacturing data platform at GE.