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Socio-technical and Domain-Driven Design architect. Facilitator of visual and collaborative modelling with Deep Democracy
Software systems often live for many years or even decades, are carefully maintained and patched again and again. But at some point the UI looks dusty, changes take forever and you want to benefit from the possibilities of modern technologies. The decision to modernize the system is made. And then comes the simplest requirement in the world, which we’ve all heard before: ‘But the new system must be able to do the same things as the old one!’ It is not surprising that we hear this requirement so often: it is simple, it can be formulated even if you know the system only superficially, and it seems to be precise. In fact, this requirement is quite nonsensical. In this talk, we will explain why the simplest requirement in the world is nonsense and provide experience, guidance, and best practices for modernization projects where the goal is to design the target state and plan the path of modernization.
One of the objectives of software architecture is the understanding and communicating complexity. We have long recognized that the most effective way to communicate complexity is via human language. But language poses a challenge when working in a team comprised of members of different (sub)cultures and nationalities, each with a native language that might not be the same as the language we are communicating in. Each of these cultures has a different perception about how to communicate effectively. By way of example, in some cultures, it is considered appropriate and respectable to use the tentative voice “perhaps we should consider trying X”. Whereas in other cultures it is the assertive voice that is valued “This is how we should do it”. Assuming that everybody in the room wants to communicate effectively, what aspects can we define that impact our design? What organizational culture fits better with what type of architecture (microservices, monolith)? And what cultural needs must these architectures and boundaries address to succeed? Join us in this interactive talk where we together explore these challenges!
Today it is the world of Data Science. Ample amount of data is available which when utilized at right time in right manner can help to forecast as well as predict in advance any untimely failures/disasters that can cause serious and fatal losses. Out of many, one area where such predictions and Predictive Analytics Software can be of great use is, manufacturing/process industries like power plants, oil and natural gas and many such more.
Predictive Analytics Software not only has components like main stream software-intensive systems, but it also has statistical algorithms, tools, techniques, mathematical components for pattern recognition as well as other techniques like machine learning, artificial intelligence, modelling etc. It also has diverse stakeholders and other connected systems.
Software Architecture is well practiced in main stream software-intensive systems like web, embedded, enterprise applications etc. However, Predictive Analytics being emerging branch, there is a lot of scope for research and enhancement in Software Architecture concepts with respect to such software systems.
With my experience working with Predictive Analytics Software for power plants, in this paper I will talk about following 3 points:
1. Benefits of practicing Software Architecture in Predictive Analytics Software
2. Challenges in using Software Architecture in Predictive Analytics Software
3. Future ahead
During my career in IT and people development I had several turning points where I either was made to use journaling techniques or experimented with them myself to successfully tackle the next challenge. Over the years I reflected why those ‘written self-reflection’ techniques are so powerful and – at the same time – they are still quite rarely used in the business context. In this workshop I will happily share my experience and my findings backend by a scientific psychological model with you! You want to leverage your resources?
You want to change habits in your life’s “departments”? You want to harvest outstanding outcomes – at work and beyond? YES? Then join us to get ready for ACTion and be inspired how to leverage journaling techniques – at work & beyond. We’ll even use our hands, hearts and minds to directly try out some of them!
Recent research summarised in the book Accelerate points to a set of practices that lead to high software development organisation performance. Simultaneously, research from the Santa Fe institute on Complex Adaptive Systems over the last 20 years seems to point to a grand unified theory of organisational design. So have we cracked it? Do we now have the answer to the question: how do we create and scale high performing software and organisations? In this talk, James explores the relationships between team structure, software architecture and the emergent phenomenon of complexity science.
Today it is the world of Data Science. Ample amount of data is available which when utilized at right time in right manner can help to forecast as well as predict in advance any untimely failures/disasters that can cause serious and fatal losses. Out of many, one area where such predictions and Predictive Analytics Software can be of great use is, manufacturing/process industries like power plants, oil and natural gas and many such more.
Predictive Analytics Software not only has components like main stream software-intensive systems, but it also has statistical algorithms, tools, techniques, mathematical components for pattern recognition as well as other techniques like machine learning, artificial intelligence, modelling etc. It also has diverse stakeholders and other connected systems.
Software Architecture is well practiced in main stream software-intensive systems like web, embedded, enterprise applications etc. However, Predictive Analytics being emerging branch, there is a lot of scope for research and enhancement in Software Architecture concepts with respect to such software systems.
With my experience working with Predictive Analytics Software for power plants, in this paper I will talk about following 3 points:
1. Benefits of practicing Software Architecture in Predictive Analytics Software
2. Challenges in using Software Architecture in Predictive Analytics Software
3. Future ahead
As a growing number of industries turn their focus on climate change, innovating in order to do their part on the journey to Net Zero – how does software engineering fit into this picture, with the industry handcuffed to it’s consumption of resources? In this talk we will dive into the various resources required to develop and host modern software, as well as the ways in which we can reduce our impact on the environment through architectural choices.
Embedded Real-Time Systems, especially such with specialized hardware, pose a lot of additional architectural challenges compared to commercial software architectures: Technology trade-offs between hardware and software, qualities like availability that can only be solved in conjunction between hardware and software, hard real-time requirements, … Using an industrial system (called Traffic Pursuit System, mounted in police cars to trace traffic offenders) as an example, this talk demonstrates hardware/software-codesign and its documentation in the proven arc42 template. We will demonstrate how the template can be used to capture both, hardware and software design (and their alignment), how hardware and software interfaces can be modeled and how system design decisions can be captured.
Special emphasis will be put on demonstrating architectural decisions to fulfil specific quality requirements (like accuracy of the measurement, robustness of the overall system and ease of use for police officers).
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