An Efficient Industrial Big-Data Engine

Val, Pablo Basanta

doi:10.1109/tii.2017.2755398

Cited by 40 publications

(28 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Concentrated (cloud / offline) [319], [322], [324] Distributed (edge / real-time) [315], [316], [318], [320] Hybrid [317], [321], [323] of sensing systems, outer and inner gateway processors, and central processors for the deployment of big data analytics applications in IIoT. In [323], the authors analyze the relationship between the data processing and the energy consumption through investigating the content correlation of the captured data.…”

Section: Computation and Data Analytics Articlesmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

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.

show abstract

Section: Computation and Data Analytics Articlesmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

show abstract

“…Most of the applications described in Section 3 require a multitude of data to be enabled, data that are later processed according to big data principles, with specific engines such as those described in [48][49][50]. In order to collect these different types of unstructured data coming from such heterogeneous sources, the MCS paradigm can be used.…”

Section: Using Mcs Inmentioning

confidence: 99%

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

Pilloni

2018

Future Internet

110

View full text Add to dashboard Cite

We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents the main aspects related to Industry 4.0, the technologies that will enable this revolution, and the main application domains that will be affected by it. The effects that the introduction of Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data will have on industrial processes will be discussed. The main objectives will be represented by improvements in: production efficiency, quality and cost-effectiveness; workplace health and safety, as well as quality of working conditions; products' quality and availability, according to mass customisation requirements. The paper will further discuss the common denominator of these enhancements, i.e., data collection and analysis. As data and information will be crucial for Industry 4.0, crowdsensing and crowdsourcing will introduce new advantages and challenges, which will make most of the industrial processes easier with respect to traditional technologies.

show abstract

“…Big Data software architectures have to deal with several challenges. First, they need to handle huge amounts of data that cannot be processed with standard infrastructures [3]. Software solutions like Apache Spark and Hadoop come with obvious advantages in designing Big Data systems [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Specification of a Software Architecture for Cross-Sectoral Big Data Applications

Arapakis

Becerra

Boehm³

et al. 2019

2019 IEEE World Congress on Services (SERVICES)

View full text Add to dashboard Cite

The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim to address the needs of IT experts, while Big Data analytics tools remain unavailable to non-expert users to a large extent. In this paper, we report on the work-in-progress carried out in the context of the H2020 project I-BiDaaS (Industrial-Driven Big Data as a Self-service Solution) which aims to address the above challenges. The project will design and develop a novel architecture stack that can be easily configured and adjusted to address crosssectoral needs, helping to resolve data privacy barriers in sensitive domains, and at the same time being usable by non-experts. This paper discusses and motivates the need for Big Data as a self-service, reviews the relevant literature, and identifies gaps with respect to the challenges described above. We then present the I-BiDaaS paradigm for Big Data as a self-service, position it in the context of existing references, and report on initial work towards the conceptual specification of the I-BiDaaS software architecture.

show abstract

An Efficient Industrial Big-Data Engine

Cited by 40 publications

References 42 publications

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

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

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

Towards Specification of a Software Architecture for Cross-Sectoral Big Data Applications

Contact Info

Product

Resources

About