This is part two of our series on how we designed and implemented a scalable, highly-available and fault-tolerant microservice-based Image Editor. This part depicts how we went from a basic Docker Compose setup to running our application on our own »bare-metal« Kubernetes cluster.
This is part one of our series on how we designed and implemented a scalable, highly-available and fault-tolerant microservice-based Image Editor. The series covers the various design choices we made and the difficulties we faced during design and development of our web application. It shows how we set up the scaling infrastructure with Kubernetes and what we learned about designing a distributed system and developing a production-grade Kubernetes cluster running on multiple nodes.
When I was invited to a design thinking workshop of the summer school of Lucerne – University of Applied Sciences and Arts, I made my first experience with the end user interaction part of Industry 4.0. It was an awesome week with a lot of great people and made me interested in the whole Industry 4.0 theme. So when we did projects in the lecture of cloud development I was sure to do a production monitoring project. Continue reading
The idea for this project occurred to me while I was listening to my sister share her vision for her recently started blog: To create a platform where writers of different ethnicity can publish texts in their native languages and exchange their stories with people from all over the world. Conquering the language barrier and making the texts available at least in the three most prominent languages – German, English and Arabic – requires the involvement of translators who are fluent in at least two of the demanded languages. Anyone who has ever attempted to make a translated text sound natural knows that this is no easy feat and can take up to many hours of finding the perfect balance between literal translation and understandable text.
This is where I saw room for improvement. Nowadays, machine translation tools have reached a decent level of fluency, despite not being able to capture the intricacies of different linguistic styles. Combining them with people who have a basic understanding of the source language can help speed up the process and reduce the effort considerably. Continue reading
For the Dev4Cloud lecture at HdM Stuttgart, we created a simple Go/NodeJS/React App, which helps people to keep track of often used words during presentations. In a presentation setting, most people tend to use too many fill words and to train against this, we want to introduce our presentation counter to you.
What I learned in building the StateOfVeganism ?
By now, we all know that news and media shape our viewson these discussed topics. Of course, this is different from person to person. Some might be influenced a little more than others, but there always is some opinion communicated.
Considering this, it would be really interesting to see the continuous development of mood communicated towards a specific topic or person in the media.
As part of the lecture “Software Development for Cloud Computing” we were looking for a solution, how a user can get basic assistance within our existing virtual reality game AIRA. The primary objective was a maximum of user-friendliness, while avoiding an interruption of the immersive gaming experience. It is also important to keep in mind, that the user is on its own and any kind of support from outside is usually not possible.
Moreover, considering that within virtual reality applications generally no conventional input devices will be available and therefore a keyboard is not an option. If we still following up this idea, many people may think next of an on-screen keyboard, as they know it from their smart TV at home, which might be operated by a game controller. Although such an approach would be contrary to a high ease of use and majority of implementations are quite crippled as well as hard to use.
So, what would be obvious and take all previous considerations into account? Simply think of something that each of us is carrying along at any time – the own unique voice. According to this we decided to implement a personal voice assistant into our game. In the following, it can be seen that the individuality of each human voice leads into a lot of difficulties we have to take care of.
In the following, it will be explained in detail how we implemented a personal voice assistant using multiple Watson services, which are part of the IBM Bluemix cloud platform. Especially fundamental problems we run into will be discussed and then possible approaches will be pointed out.
Howdy, Geeks! Ever frustrated by public transportation around Stuttgart?
Managed to get up early just to find out your train to university or work is delayed… again?
Yeah, we all know that! We wondered if we could get around this issue by connecting our alarm clock to some algorithms. So we would never ever have to get up too early again.
Well, okay, we’re not quite there yet. But we started with getting some data and did some hardly trustworthy hypothesis of prediction on it. In the end it’s up to you if you gonna believe it or not.
To give you a short overview, here are the components that are involved in the process. You will find the components described in more details below.
A view parts in short:
1. crawler and database – get and store departure information
2. visualization – visualizes the delays on a map
3. statistical analysis – some statistical analysis on the delays over a week
4. continuous delivery – keep the production system up to date with the code
New data is created every second. Just on Google the humans preform 40,000 search queries every second. By 2020 Forbes estimate 1.7 megabytes of new information will be created every second for every human on our planet.
However, it is about collecting and exchanging data, which then can be used in many different ways. Equipment fault monitoring, predictive maintenance, or real-time diagnostics are only a few of the possible scenarios. Dealing with all this information, creates certain challenges for stream processing of huge amounts of data is among them.
Improvement of technology and development of big scaling systems like IBM Bluemix it is now not only possible process business or IoT data, it is also interesting to analyze complex and large data like sport studies. That’s the main idea of my application – collect data from a 24-hour swimming event to use real time processed metrics to control event and athletes flow.
In this article explains how to integrate and use the IBM tools for stream processing. We explore IBM Message Hub (for collecting streams), the IBM Streaming Analytics service (for processing events) and IBM Node.JS Service (for visualization data).