Industrial IoT and Analytics Platform Sample Clauses

Industrial IoT and Analytics Platform responsible for developing the MIDIH platform for IoT and Analytics, having into account both Data-in-Motion (DiM) and the Data-at-Rest (DaR). Based on the first prototype delivered in the first iteration, the objective in this new iteration has been to provide an improved AI-enabled data analytics platform based on APACHE and FIWARE open source components, providing an interoperability model between the two different technologies. The output of these tasks delivers a collection of functional components for implementing the final MIDIH reference architecture, described in D4.2, for the Cross-Border Industrial Experiments in WP5 (and Open Calls winners). Like in the first iteration, the developed open source components enhance, extend or integrate existing Open Source solutions available on the market or developed within previous European research projects such as BEinCPPS (Phase II I4MS). Table of Contents‌ Executive summary 5 Table of Contents 6 List of Figures 7 1 Introduction 9
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Industrial IoT and Analytics Platform responsible for developing the MIDIH platform for IoT and Analytics, having taken into account both data-in-motion and the data-at-rest. Main challenge is implementing this platform for analytics based on FIWARE and on worldwide known Open Source projects and Foundations, such as APACHE; and providing interoperability model between the two different technologies. The output of these tasks delivers a collection of functional components implementing the MIDIH reference architecture (D4.1) for the Cross-Border Industrial Experiments in WP5 (and Open Calls winners). In the spirit of an Innovation Action, the developed open source components enhance, extend or integrate existing Open Source solutions available on the market or developed within previous European research projects such as BEinCPPS (Phase II I4MS).
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Industrial IoT and Analytics Platform. (T4.4)‌ MIDIH T4.4 responsible for the Data Analytics Platform (Figure 5), has developed two basic lanes for Industrial IoT and Industrial Analytics, one FIWARE-based and the other based on worldwide known Open Source projects and Foundations, such as APACHE. The former represents an EU- funded reference implementation, fully compatible with any Context Information data source; the latter aims at achieving interoperability with existing worldwide projects using other Open Source middleware. At the same time, it has to allow and facilitate as main challenge the interoperability model and implementation between the two Lanes.
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Industrial IoT and Analytics Platform. Apache lane‌ The Platform covers an advanced set of functionalities in order to support the challenging manufacturing scenarios the project is targeting. On the application and data processing levels, the Platform will offer a rich feature set for cloud enablement and generic data treatment, which can be used to rapidly assemble end-to-end IoT applications for industrial systems automation, predictive maintenance, and remote monitoring. The following picture shows the initial APACHE components selected to cover the functionalities offered by the Industrial IoT and Analytics Platform. 4.1.1 Background components‌ D2Lab (xxxxx://x0xxx.xxxxxxxxx.xxx/) is the proprietary framework by NISSATECH for developing big data analytics solutions. It can be treated as a personalized diagnostic laboratory for industrial cases. The framework has been applied for the development of several efficient and scalable systems for data-driven quality control. Figure 7 shows the conceptual architecture of the framework. The main innovation is related to the data-driven management of usual/unusual behavior that enables us to treat unusualness as first-class citizens and consequently to provide support for the whole life cycle of the usual/unusual (anomalous) behavior.
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Industrial IoT and Analytics Platform. FIWARE lane‌ The FIWARE lane is made up of a set of micro-services, aptly-named Generic Enablers (GE), that combined can provide valuable functionalities. This project aims to provide useful tools that will be deployed for manage and monitor industrial scenarios the project is targeting. As is depicted in Figure 8, the tools must cover from the data gathering, here named as Data-In-Motion, to the data storage and treatment, named Data-at-rest. 4.2.1 Background components‌ The project starts from the well-defined FIWARE IoT Stack (xxxx://xxxxxx-xxx- xxxxx.xxxxxxxxxxx.xx/xx/xxxxxx/). This architecture fits the majority of the functionalities depicted in the previous Figure 8, and serves as base architecture for the FIWARE lane deployment. Besides this, other FIWARE components are added, extending the functionality in order to cover the entire set of capabilities required. For instance, tools like Knowage, CKAN or Kurento are open source tools that can be used within the FIWARE ecosystem. 4.2.2 Foreground components‌ As described, the FIWARE IoT Stack is focused in data gathering from IoT devices. The innovation presented is the architecture’s evolution from an IoT-based scenario to a well-defined, two- layer, industrial scenario. This new scenario, with a new and more developed architecture, must serve all the steps from data gathering or data storage to treatment and processing. Therefore, a more detailed and ambitious architecture will be presented as result. Besides this, a complete integration between the aforementioned APACHE lane and this FIWARE lane will be provided as an interesting output.
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