Sending Data from Oracle IoT to a Mobile Device Using Node.js

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In this learning path, you will create a Node.js application to integrate Oracle IoT Cloud Service and Oracle Mobile Cloud Service. Take the course here.

Five Ways Blockchain Is Changing Higher Ed
For one, blockchain’s ability to manage, share, and protect digital content makes it ideal for helping researchers and educators create intellectual property, share it, and still control the way it’s used. Professors, for instance, could be rewarded based on the actual use, and reuse, of their teaching materials, much as how they’re rewarded based on citations in research papers and journals. Four more advantages.

 

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Another blockchain: installing Ethereum on Oracle Cloud by Lonneke Dikmans

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After installing MultiChain on Oracle Compute Cloud, and playing around with HyperLedger on the Oracle Blockchain Cloud service, I now ran into a case where Ethereum was used.
This blog post describes how I installed a Ethereum node on Oracle Cloud Infrastructure.

Prerequisites
  • An account on Oracle Cloud with Administrator rights
  • You have generated an ssh key-pair
  • You are logged in to your cloud account
Create Compartment

Create a compartment with the name Ethereum to separate this from your other infrastructure. You can find this under "Identity".

Create a Virtual Cloud Network
  1. Navigate to Virtual Cloud Network, by selecting Network from the Menu
  2. Select the compartment you just created
  3. Click "Create Virtual Cloud Network"
  • The compartment will default to the compartment you just selected
  • Name: ethereum-network
  • select "Create Virtual Cloud Network plus related resources" to quickly get up and running
  • Click "Create"

A dialog is displayed that shows you what has been created, after a few seconds.

Create compute nodes

For this example I will create 3 nodes on 3 separate VMs. Read the complete article here.

 

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Integration and Blockchain – Heart of the Digital Economy by Steve Quan

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Digital transformation is inevitable if you are looking to thrive in today’s economy.  In the first three podcasts of the series “Integration: Heart of the Digital Economy,” we heard about how application and data integration play a central role in business digital transformations.

In this new podcast, you’ll learn how the combination of Blockchain technologies, APIs, application and data integration extend enterprise backend systems for B2B or B2C business models.  Blockchains are a series of secured transactions between two parties called a block.  These blocks are linked together and used as a distributed ledger to store any information used in a business transaction. So as your company starts to develop blockchain specific applications, you’ll need to carefully plan and link your blockchain strategy with your integration strategy. This ensures that you can push (and pull) corporate data into (or from) their Blockchains.

Learn how different industries use Blockchain technology.  Listen to the podcast  Blockchain Integration: Powering the Smart Economy.

 

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At GE Digital, Hackfest Chases Big Ideas

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“We do hackathons a bit differently,” explains GE Digital’s Kamil Litman. “Instead of use cases being fed to the teams, we want part of it to be the ideation phase.” Not long ago, the company created a way to give energy buyers real-time pricing insights at GE oil and gas subsidiary Baker Hughes. Also helpful: Oracle demonstrated how other teams have used cloud technologies for their own projects. What came out of the three-day hackathon

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Podcast Series: Tomorrow’s Enterprise, Today

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Throughout the world and across every industry, cutting-edge technologies like artificial intelligence and machine learning are disrupting the business landscape. Organizations are gearing up to prepare for the future today. But how do you continuously innovate, continuously adapt, continuously secure and continuously stay ahead of the curve? Tune in to this podcast series where we’ll dig deeper into how some organizations are taking this challenge head-on to drive rapid and continuous innovation to better serve their customers, employees and partners and the different strategies and paths of technology adoption experts are seeing today.

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Machine Learning — Getting Data Into Right Shape by Andrejus Baranovskis

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When you build machine learning model, first start with the data — make sure input data is prepared well and it represents true state of what you want machine learning model to learn. Data preparation task takes time, but don’t hurry — quality data is a key for machine learning success. In this post I will go through essential steps required to bring data into right shape to feed it into machine learning algorithm.

Sample dataset and Python notebook for this post can be downloaded from my GitHub repo.

Each row from dataset represents invoice which was sent to customer. Original dataset extracted from ERP system comes with five columns:

customer — customer ID

invoice_date — date when invoice was created

payment_due_date — expected invoice payment date

payment_date — actual invoice payment date

grand_total — invoice total

Read the complete article here.

 

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Oracle Tech Talk: Blockchain: Beyond the Hype with a Developer

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Blockchain technology is in the headlines daily. Is it a panacea for the world’s problems or just the latest tech craze? Join one of the original Bitcoin mining leaders, Emmanuel Abiodun, Software Architect at Oracle, for this informal look at Blockchain and its true value. In this informal tech talk, Mr. Abiodun will separate the reality from the hype. For details please visit the registration page here.

 

 

PaaS Partner Community

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Developing an IoT Application Powered with Analytics

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Learn how to develop an Internet of Things (IoT) application on Oracle IoT Cloud Service that processes your sensor data, predicts future events from the historical sensor data, and persists the analyzed results. Take the course here.

 

PaaS Partner Community

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Our new product – Katana 18.1 (Machine Learning for Business Automation) by Andrejus Baranovskis

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Big day. We announce our brand new product – Katana. Today is first release, which is called 18.1. While working with many enterprise customers we saw a need for a product which would help to integrate machine learning into business applications in more seamless and flexible way. Primary area for machine learning application in enterprise – business automation

Katana offers and will continue to evolve in the following areas:
1. Collection of machine learning models tailored for business automation. This is the core part of Katana. Machine learning models can run on Cloud (AWS SageMaker, Google Cloud Machine Learning, Oracle Cloud, Azure) or on Docker container deployed On-Premise. Main focus is towards business automation with machine learning, including automation for business rules and processes. Goal is to reduce repetitive labor time and simplify complex, redundant business rules maintenance
2. API layer built to help to transform business data into the format which can be passed to machine learning model. This part provides API to simplify machine learning model usage in customer business applications. Read the complete article here.

 

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Value of Technical Capability Models by Phil Wilkins

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The use of Technical Capability models is not something I have seen a lot of use of, which is a little unfortunate as they can provide tremendous insight into an organizations IT needs.

Typically you want to use the Technical capability model to be used in conjunction with a business capability model, and this is where things can get tricky as developing the business views can take time.  I came across this short video which focuses on the more business aspect but helps explain the ideas behind the models:

Note how the model is largely groups of capabilities that happen in the business. Underlying this kind of diagram you would have a brief explanation of each capability.  If you want to go all out on EA modelling then you can link the capabilities to the documented associated processes etc.

Independently, the ideal is to then identify the technical capabilities that are likely to be needed. This will provide a similar looking model. The technical capabilities are probably best drawn from industry best practices, and specific business needs. The model should be completely product agnostic. The real value comes in by then mapping the technical capabilities to which business capabilities use. Read the complete article here

 

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