Industrial Pannel
*My own comments are marked with asterisks
The panelists shall respond the following questions:
Panelists:
– Karin Breitman – Head of Analytics Centre of Excellence at Rio Tinto
– Oscar Pastor – Professor at Universitat Politécnica de València, chair
– C. Mohan – IBM Fellow, IBM Almaden Research
I. Oscar Pastor
1) What are the inhibitors of modeling in practice?
*My own comments are marked with asterisks
The panelists shall respond the following questions:
- What are the main inhibitors of modelling in practice?
- What could be done to improve the popularity of conceptual modelling in practice?
- What lessons can be learn from teaching conceptual modelling in practical settings?
Panelists:
– Karin Breitman – Head of Analytics Centre of Excellence at Rio Tinto
– Oscar Pastor – Professor at Universitat Politécnica de València, chair
– C. Mohan – IBM Fellow, IBM Almaden Research
I. Oscar Pastor
1) What are the inhibitors of modeling in practice?
- Software Engineering is not really recognized in practice as a true engineering.
- More as a handcrafts-centered activity (not technical/systematic)
- Strong dependence on skilled programmers.
You need to be precise. And precision means ontology. We really must push for a comprehensive understanding of the things we work with.
2) What could be done to improve the popularity of conceptualm odeling in practice?
- Conceptual Programming (CP)-based tools
- Assess flexibility,e fficiency and effectiveness of those tools
- Emphasizing the relevance of CM in SE teaching.
Even when tools are available, they do not achieve/allow maximum efficiency, sometimes because of the way the have been engineered.
*A lot of the tools for the latest techniques are academic ones and there are a lot of bugs! Not really products
What is an especially promising research direction in CM
- Conceptual Modeling of Life (Genome)
- The role of CM to guide/lead the digital transformation of our society
- From Homo Sapiens to Homo Genius
Need to conduct well the digitalization process
What are the current methods, tools and technology in use, especially as it relates to modeling ML applications
- Explainable AI is a big opportunity for CM
- Promising areas for use Model@Run Time
- Big Data is not Schemaless!
- CM of the jhuman genome and precision medicine implications
- efficent and flexible Enterprise Modeling (EM)
- Full conceptual algiment between EM and software applications
- From Requirements to Code
The Dream (from Nicola Guarino 2008): Ontology-driven CM
He highlights the effort of Prof. D. Karaghianis and his group to promote CM practice.
As the ER community, we need to provide responses of whether CM is useful and how.
II. Karin Breitman
She works establishing analytics teams to
The problem is:
How can we make sense of the world complexities?
We must rely on CM for that. But how?
In her practice, she uses them in two ways:
1) Capturing the processes is one of the main use of CM
2) Data-driven - how do I integrate data?
BP assists us in negotiating how technology will be used. We need to have a uniform view on that to guarantee career opportunities and
There is no digital transformation without data integration.
Her company has been doing some work on that, trying to extract the data schemas from the data in a semi-automated way. We also need ontologies that are semantically rich.
In industry, we see today the co-habitation of two software models:
- companies relying on enterprise solutions, such as SAP etc. Companies are struggling to maintain that and use that effectively
- legacy system use: digital transformation is in a sense the modernization of legacy systems that have been used since always
In Industry, we need to move from project to product. In the end of projects, we end up with a "cemetery of POCs" because the developed applications do not talk to each other. We need people that are competent in abstraction, and are able to provide integrated tools.
In terms of technological evolution, what it does industrially is reducing cost. For example, the Internet has drastically lowering the cost of how things are done. Now, what AI is doing for us now is reducing the cost of prediction. That is being done by companies like Google, Amazon and others.
An application in Mineral Ore (MO) Mining:
MO Mining is about processing the ore. The more information you have, the better. So her company transformed that in a prediction problem and now predicts information by mining the data, which leads to an economy of 22 to 40 million dollars a year per mine.
The ability to created models that will support communication with clients is very important.
III. C. Mohan
He comes to IBM, which has a huge body of people working with technology in different levels, from conceptual modeling to code. One of the groups work solely with requirements
More and more focus on blockchain applications.
MDD using a Composer, in which people code in a high-level language and then this gets translated to lower-level ones.
There is a people registry, saying which person/organization is involved with each test case. And there is also an artifacts registry, indicating what kind of artifact is being managed using the blockchain model.
For instance, it may be taking a raw diamond and transforming it into a polished diamond. We must then represent how transactions happen when the diamond goes to one hand to another.
This is the Blockchain network
How do you go from an English-based contract to a more formal representation of such network?
If Google would stop working, the impact in the world would be much less than one would think. And certainly much lower than the impact of the halt of some of the legacy systems that are on for over 50 years in mills and companies.
The problem is again semantics. We must provide solutions for hardcoding the semantics into the CM.
There is a higher and higher need for providing explainable results. Explainable AI is becoming very important, while the Deep Learning approaches are black-boxes that unfortunately take decisions without providing their rationale.
Q&A
Ulrich claims that you need to reflect on concepts. Modeling means looking beyond what is and reflect on what it could be. That is really fascinating.
But then, this brings up some difficult challenges.
First, CM needs to bridge the gap from the formal world and the reality (although reality is not the better word). There are groups that focus too much in reality, but lack knowledge about CM. On the other hand, there are many people (and especially in this community) that focus too much on the formal world, without regard for reality. But we must do both!
Do we have the methods and the skills to respond to this challenge?
Conceptual Modelers should never be satisfied by what they see, but always be creative to invent new and better ways of doing things.
Karin says:
We are in a shift on how we use technology. Technology used to be embodied on something. Now it is much more pervasive. This changes practices a lot. It is important to understand how much we can delegate to the machine, of the things computers can do better than us. And then, focus on human for decision making.
Karin says we need:
- stronger philosophical background
- humanistic view - develop human skills
The important skills to be developed are:
- identifying the problem - prioritizing - and then targeting it -
Her young teams have difficulties in prioritizing
This sort of decision making will never disappear in human lives. The more technical issues may be alleviated as the tools become better.
Daniel Dennet's book: From Bacteria to Bach and Back (see the TED talk about that)
Oscar claims that we need a Conceptual Modeling Manifesto to define well what it is and what is not.
