Related papers: Enabling Big Data Analytics at Manufacturing Field…
The concept of Industry 4.0 brings a disruption into the processing industry. It is characterised by a high degree of intercommunication, embedded computation, resulting in a decentralised and distributed handling of data. Additionally,…
Industry 4.0 is a concept devised for improving the way modern factories operate through the use of some of the latest technologies, like the ones used for creating Industrial Internet of Things (IIoT), robotics or Big Data applications.…
The Industry 4.0 concept promotes a digital manufacturing (DM) paradigm that can enhance quality and productivity, that reduces inventory and the lead-time for delivering custom, batch-of-one products based on achieving convergence of…
To process data more efficiently, big data frameworks provide data abstractions to developers. However, due to the abstraction, there may be many challenges for developers to understand and debug the data processing code. To uncover the…
This paper describes our approach to the Bosch production line performance challenge run by Kaggle.com. Maximizing the production yield is at the heart of the manufacturing industry. At the Bosch assembly line, data is recorded for products…
The availability of both structured and unstructured databases, such as electronic health data, social media data, patent data, and surveys that are often updated in real time, among others, has grown rapidly over the past decade. With this…
Today's high-performance computing (HPC) systems are heavily instrumented, generating logs containing information about abnormal events, such as critical conditions, faults, errors and failures, system resource utilization, and about the…
As data volumes grow across applications, analytics of large amounts of data is becoming increasingly important. Big data processing frameworks such as Apache Hadoop, Apache AsterixDB, and Apache Spark have been built to meet this demand. A…
Cyber-Physical Systems (CPSs) employed for Industrial Automation often require the adoption of a hybrid data processing approach mediating between cloud, edge, and fog computing paradigms. Nowadays, it is possible to shift data…
As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For…
Current automation approaches in the Industry 4.0 have generated increased interest in the utilization of Integration Platforms as a Service (iPaaS) cloud architectures in order to unify and synchronize several systems, applications, and…
Industry 4.0 offers many possibilities for creating highly efficient and flexible manufacturing. To create such advantages, highly automated and thus digitized processes and systems are required. Here, most technologies known from the…
Domain Name System (DNS) is a hierarchical distributed naming system for computers, services, or any resource connected to the Internet. A DNS resolves queries for URLs into IP addresses for the purpose of locating computer services and…
In the engineering and manufacturing domain, there is currently an atmosphere of departure to a new era of digitized production. In different regions, initiatives in these directions are known under different names, such as industrie du…
Industry 4.0 is changing fundamentally the way data is collected, stored and analyzed in industrial processes. While this change enables novel application such as flexible manufacturing of highly customized products, the real-time control…
Traditional statistical and measurements are unable to solve all industrial data in the right way and appropriate time. Open markets mean the customers are increased, and production must increase to provide all customer requirements.…
As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An obvious use for…
Today, one of the biggest challenges for digital transformation in the Industry 4.0 paradigm is the lack of mutual understanding between the academic and the industrial world. On the one hand, the industry fails to apply new technologies…
This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms. It allows deep learning…
Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and…