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RapidMiner : Partners with Hivecell to Enable Real-Time AI at the Edge

RapidMiner : Partners with Hivecell to Enable Real-Time AI at the Edge
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METIS and Hivecell agree to lead maritime digitalization from the edge | Hellenic Shipping News Worldwide

METIS and Hivecell agree to lead maritime digitalization from the edge Maritime data analytics specialist METIS Cyberspace Technology has completed proof of concept trials to confirm that edge computing from Hivecell is wholly compatible with its cloud-based shipboard data acquisition solution. Completion opens the way for fully scalable Hivecell stack processing power to be used at locations onboard ship, resulting in significant bandwidth savings and improved response times for ‘smart’ ship owners and managers. In a maritime industry awash with smart devices yet tied to centralized systems management, cloud storage is increasingly connecting the dots, with processing power ‘at the edge’ enabling only information relevant to decision-making to be uploaded. On top of reducing hardware and data traffic, edge computing saves on server maintenance and training, while integration is easier.

Portnet - METIS and Hivecell agree to lead maritime digitalization from the edge

Portnet 25 Ιανουαρίου 2021 Maritime data analytics specialist METIS Cyberspace Technology has completed proof of concept trials to confirm that edge computing from Hivecell is wholly compatible with its cloud-based shipboard data acquisition solution. Completion opens the way for fully scalable Hivecell stack processing power to be used at locations onboard ship, resulting in significant bandwidth savings and improved response times for ‘smart’ ship owners and managers. In a maritime industry awash with smart devices yet tied to centralized systems management, cloud storage is increasingly connecting the dots, with processing power ‘at the edge’ enabling only information relevant to decision-making to be uploaded. On top of reducing hardware and data traffic, edge computing saves on server maintenance and training, while integration is easier.

METIS and Hivecell agree to lead maritime digitalization from the edge – gCaptain

Share this article Press Release – Maritime data analytics specialist METIS Cyberspace Technology has completed proof of concept trials to confirm that edge computing from Hivecell is wholly compatible with its cloud-based shipboard data acquisition solution. Completion opens the way for fully scalable Hivecell stack processing power to be used at locations onboard ship, resulting in significant bandwidth savings and improved response times for ‘smart’ ship owners and managers. In a maritime industry awash with smart devices yet tied to centralized systems management, cloud storage is increasingly connecting the dots, with processing power ‘at the edge’ enabling only information relevant to decision-making to be uploaded. On top of reducing hardware and data traffic, edge computing saves on server maintenance and training, while integration is easier.

WPX Energy Aims to Improve Drilling and Completion Operations with Hivecell Edge-as-a-Service for Confluent

Share: New technology solution will enable leading energy company to generate business-relevant data from drilling and completion operations BEACON, N.Y. (PRWEB) December 10, 2020 Hivecell, the Edge-as-a-Service company, announced today that WPX Energy has implemented the Hivecell Edge-as-a-Service for Confluent solution. The solution is designed for true real-time decision making and future potential closed-loop control optimization, which the company hopes to achieve in 2021 at its drilling and completion operations in the oil fields of West Texas and North Dakota. As oil companies are increasingly under pressure to escalate drilling operation efficiency, WPX Energy uncovered that machine learning (ML) and artificial intelligence (AI) are key to achieving optimal results. The company is currently building its data pipeline and edge stream process solution leveraging Confluent, an enterprise-ready event streaming platform that manages the raw data from Apache Kafka. Eventually,

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