What’s new in Oracle IoT Production Monitoring by Harish Gaur

Continuing with the “What’s new” series, we will look at what’s new in Oracle Production Monitoring Cloud over the last 3 months across three key innovation themes. For the uninitiated, Oracle IoT Production Monitoring (IoT PM application) monitors the performance of your factories at different levels, starting from a global perspective to then drill down to the health of specific machines. It gives you real-time visibility into your production process and helps you diagnose and predict production issues so that you can increase the uptime of your factories. It also helps you schedule maintenance so that you can minimize the disruption to your daily operations.

Digital Thread

IoT Apps enable frictionless hand-off across different stages of the supply chain, beginning with design & production to transport to field use.

The integration with Oracle Manufacturing Cloud is now available. Why is this important? Oracle Manufacturing Cloud enables engineers to quickly define the necessary data for their plant hierarchy, and create work definitions and design production process. With this new integration, Oracle IoT PM app can download work orders from Oracle Manufacturing Cloud, associate specific machines with the work orders, and view work orders in the factory view. As a factory manager, you can load the production plan by work orders and track their progress.

The integration with Oracle Maintenance Cloud is now available. Why is this important? Using Oracle Maintenance Cloud, now it’s possible to import machines from the SCM Maintenance Cloud into Oracle IoT Production Monitoring Cloud Service. When an incident is created against an imported machine in Oracle IoT Production Monitoring Cloud Service, the incident automatically translates into a work order in the SCM Maintenance Cloud. For example, if a threshold rule triggers an incident when a device associated with a machine is overheating, a work order corresponding to the incident automatically gets created in the SCM Maintenance Cloud. Incidents can be triggered by a business rule in reaction to an event (machine has overheated), by proactive detection of anomalies behavior (machine may overheat) or by predicting the future state of the shop floor machine (machine will overheat in 48 hours from now). Read the complete article here.

PaaS Partner Community

For regular information on Oracle PaaS become a member in the SOA & BPM Partner Community for registration please visit www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center.

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Technorati Tags: SOA Community,Oracle SOA,Oracle BPM,OPN,Jürgen Kress

Oracle Cloud – Using AI cloud Platform to find a parking spot by Johan Louwers

One of the new and upcoming parts of the Oracle cloud is the Oracle AI Cloud platform. In effect this is a bundle of pre-installed frameworks and libraries who are tuned to run on the Oracle cloud infrastructure. One of the deployments in the Oracle AI Cloud Platform is OpenCV. When you are working with incoming visual data this might be of much interest to you.

OpenCV (Open Source Computer Vision) is a library of programming functions mainly aimed at real-time computer vision. Officially launched in 1999, the OpenCV project was initially an Intel Research initiative to advance CPU-intensive applications, part of a series of projects including real-time ray tracing and 3D display walls.

Example usecase
as an example usecase for using OpenCV from the Oracle AI Cloud Platform we like to outline a theoretical case where on a regular base pictures of a "old fashion" parking space at an airport are being uploaded to OpenCV. based upon the images that are being send to OpenCV on the Oracle AI Cloud Platform the system can detect on which part of the parking area most open spots are and direct visitors to this area. Read the complete article here.

PaaS Partner Community

For regular information on Oracle PaaS become a member in the SOA & BPM Partner Community for registration please visit www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center.

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Prepping for the Oracle AI Cloud: Machine Learning by Léon Smiers

This blog is part of a series on the introduction of the Oracle AI Cloud as part of the Oracle PaaS offering. In the first blog, The business case for the Oracle AI Cloud, we describe the capabilities and usage scenarios. The second blog, Prepping for the Oracle AI Cloud: libraries and tools, provides an overview of Python and the available data science and machine learning Libraries in the Oracle AI Cloud. In this blog, we will we will delve into the basics of machine learning and take a detailed look at the scikit-learn library in order to be prepared for the next blog on the “Deep Learning Frameworks” feature of the Oracle AI Cloud.

Machine Learning

Machine learning makes it easier to extract answers from data. It uses models and data, lots of data, to train a model in making a prediction. The goal of this training is to create an accurate model that answers (or predicts) our questions correctly based upon a set of measurements of specific data features. The distribution of the features then helps in finding groupings, also known as classifications. These classifications then in turn give input to the prediction. By continuously evaluating the results, we are able to improve the learning algorithm. Read the complete article here.

PaaS Partner Community

For regular information on Oracle PaaS become a member in the SOA & BPM Partner Community for registration please visit www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center.

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Technorati Tags: SOA Community,Oracle SOA,Oracle BPM,OPN,Jürgen Kress