Data Management in Industry 4.0: State of the Art and Open Challenges

Abstract

Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

A Wireless Sensors Network for Monitoring the Carasau Bread Manufacturing Process

“…The data-stream of the images acquired by nodes C 1 and C 2 in Figure 1 requires a higher transfer rate. Alternatively, the information must be elaborated to thoroughly reduce the dimensionality of the problem [51]. It is worth stressing the fact that the effect of the air temperature and humidity on the wireless communications can be relevant during the transmission, as found in [23].…”

“…The fast digitalization trend of Industry 4.0 must cope with the fact that, firstly, some basic in-house competencies may be lacking; e.g., installing the setup of sensors or structuring/interrogating a database with a big amount of data [26]. Furthermore, the post-processing and statistical elaboration for the heterogeneous ensemble of information derived from the WSN is not trivial [51]. This is why a simple, but rather effective open-source tool can be used to inform the stakeholders about the monitored parameters, while informing them about statistics or detected events.…”

What is knowledge in Industry 4.0?

“…technologies have coined a concept of manufacturing systems called the fourth industrial revolution (popularly known as Industry 4.0 or smart manufacturing). [3][4][5][6][7][8][9] In Industry 4.0, humans, technology, and organizations are integrated in both horizontal and vertical manners using advanced information and communication technologies. 3,4 The integration must result in some intelligent enables that help achieve the manufacturing tasks through data integration from agile sources.…”

“…3,4 The integration must result in some intelligent enables that help achieve the manufacturing tasks through data integration from agile sources. 5 At the same time, the data must be transformed into knowledge, 9 enabling seamless integration between physical and cyber spaces. 7,8 This results in some embedded systems (eg, cyberphysical systems) that can perform high-level cognitive tasks such as monitoring, understanding, predicting, deciding, acting, and adapting.…”

“…In general, knowledge makes a vast ecosystem integrating human learning, machine learning, logical inferences (deduction, induction, and abduction), experimental data, analytical results, simulations, algorithms, creative thinking, and cognitive reflections. In most case, data and knowledge are used interchangeably (eg, compare the articles 5,8,9,[13][14][15][16] ). The myriad proximal and distal interactions of knowledge among the abovementioned entities result in heavy computation.…”