A Case for Integrated Data Processing in Large-Scale Cyber-Physical Systems

Glebke, René; Henze, Martin; Wehrle, Klaus; Niemietz, Philipp; Trauth, Daniel; Mattfeld, Patrick; Bergs, Thomas

doi:10.24251/hicss.2019.871

Cited by 35 publications

(14 citation statements)

References 41 publications

(40 reference statements)

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“…Interconnected Infrastructure. An Interconnected Infrastructure for the WWL is challenged by increasingly high data rates in the manufacturing industry, where sensors can generate data in the range of giga-to peta-bytes per second [55]. These data rates are problematic for storage and semantic analysis in real-time, as well as for sending these vast amounts of data within the WWL.…”

Section: Vision Of the Internet Of Production (Iop)mentioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production , two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI . Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure . Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

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Section: Vision Of the Internet Of Production (Iop)mentioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

Self Cite

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“…Manufacturing is expected to compile vast amounts of process and product data in the near future [8,27]. Consequently, to enable automation and autonomous decision-making, we have to deal with associated big-data challenges [28,29] that are imminent due to virtually infinite volumes of available sensor data and the increased need for high-frequency sensing [1,2].…”

Section: Motivation and Potentialsmentioning

confidence: 99%

The Road to Accountable and Dependable Manufacturing

et al. 2021

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The Internet of Things provides manufacturing with rich data for increased automation. Beyond company-internal data exploitation, the sharing of product and manufacturing process data along and across supply chains enables more efficient production flows and product lifecycle management. Even more, data-based automation facilitates short-lived ad hoc collaborations, realizing highly dynamic business relationships for sustainable exploitation of production resources and capacities. However, the sharing and use of business data across manufacturers and with end customers add requirements on data accountability, verifiability, and reliability and needs to consider security and privacy demands. While research has already identified blockchain technology as a key technology to address these challenges, current solutions mainly evolve around logistics or focus on established business relationships instead of automated but highly dynamic collaborations that cannot draw upon long-term trust relationships. We identify three open research areas on the road to such a truly accountable and dependable manufacturing enabled by blockchain technology: blockchain-inherent challenges, scenario-driven challenges, and socio-economic challenges. Especially tackling the scenario-driven challenges, we discuss requirements and options for realizing a blockchain-based trustworthy information store and outline its use for automation to achieve a reliable sharing of product information, efficient and dependable collaboration, and dynamic distributed markets without requiring established long-term trust.

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“…Approaches such as cloud-based data storage methods have created the centralized approach, shifting the way in which franchisers interact with franchisees [32,33]. However, such systems (e.g., Programmable Logic Controllers (PLCs)) are expected to be too limited in their accessibility to keep up with the growing demand for higher level integration [43] and information transparency for all relevant stakeholders [44,45].…”

Section: Literature Reviewmentioning

confidence: 99%

Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology

Sun

Zheng

Villalba-Díez

et al. 2020

Sensors

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Information-intensive transformation is vital to realize the Industry 4.0 paradigm, where processes, systems, and people are in a connected environment. Current factories must combine different sources of knowledge with different technological layers. Taking into account data interconnection and information transparency, it is necessary to enhance the existing frameworks. This paper proposes an extension to an existing framework, which enables access to knowledge about the different data sources available, including data from operators. To develop the interoperability principle, a specific proposal to provide a (public and encrypted) data management solution to ensure information transparency is presented, which enables semantic data treatment and provides an appropriate context to allow data fusion. This proposal is designed also considering the Privacy by Design option. As a proof of application case, an implementation was carried out regarding the logistics of the delivery of industrial components in the construction sector, where different stakeholders may benefit from shared knowledge under the proposed architecture.

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A Case for Integrated Data Processing in Large-Scale Cyber-Physical Systems

Cited by 35 publications

References 41 publications

A Computer Science Perspective on Digital Transformation in Production

A Computer Science Perspective on Digital Transformation in Production

The Road to Accountable and Dependable Manufacturing

Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology

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