Using Property Graphs to Segment Time-Series Data

Digitization of industrial processes requires an ever increasing amount of resources to store and process data. However, integration of the business process including expert knowledge and (real-time) process data remains a largely open challenge. Our study is a first step towards better integration of these aspects by means of knowledge graphs and machine learning. In particular we describe the framework that we use to operate with both: conceptual representation of the business process, and the sensor data measured in the process. Considering the existing limitations of graph data storage in processing large time-series data volumes, we suggest an approach that creates a bridge between a graph database, that models the processes as concepts, and a time-series database, that contains the sensor data. The main difficulty of this approach is the creation and maintenance of the vast number of links between these databases. We introduce the method of smart data segmentation that i) reduces the number of links between the databases, ii) minimizes data pre-processing overhead and iii) integrates graph and time-series databases efficiently.