A three level hierarchical architecture for an efficient storage of industry 4.0 data

Abstract

The increasing interest among manufacturers in monitoring and analyzing industrial systems is generating a problem related to the considerable costs associated with the storage of the captured data. This paper presents a three level hierarchical architecture for Industry 4.0 time-series data storage on cloud environments that helps to manage and reduce those costs. In the first level, new raw time series can be stored for a short-period of time on electronic non-volatile storage such as Solid-State Drives (SSDs). In the second level, recent time series can be stored for a medium-period of time on magnetic Hard Disk Drives (HDDs). In the third level, a reduced representation of the time series obtained by applying time series reduction techniques can also be stored in HDDs for a longer period of time.The main contribution of this paper is related to the third level of the architecture, that allows to decrease the required data storage resources by storing reduced representations of time-series data, without almost hampering the use of those data for further analysis purposes. The proposed architecture, has been implemented by using some of the top Database Management Systems (DBMSs) from four different categories: Time series DBMS, Wide column stores, Document stores and Graph DBMS. It has been tested by using industrial time series coming from a real manufacturing environment, and with four different types of queries proposed by domain experts. The performance results regarding storage space, storage costs and total query time for each DBMS are shown and contrasted in this paper.