Context-Aware Analytics in MOM Applications
  Manufacturing Operations Management (MOM) systems are complex in the sense
that they integrate data from heterogeneous systems inside the automation
pyramid. The need for context-aware analytics arises from the dynamics of these
systems that influence data generation and hamper comparability of analytics,
especially predictive models (e.g. predictive maintenance), where concept drift
affects application of these models in the future. Recently, an increasing
amount of research has been directed towards data integration using semantic
context models. Manual construction of such context models is an elaborate and
error-prone task. Therefore, we pose the challenge to apply combinations of
knowledge extraction techniques in the domain of analytics in MOM, which
comprises the scope of data integration within Product Life-cycle Management
(PLM), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems
(MES). We describe motivations, technological challenges and show benefits of
context-aware analytics, which leverage from and regard the interconnectedness
of semantic context data. Our example scenario shows the need for distribution
and effective change tracking of context information.
