What are the Ontological Foundations of Simulation Modeling - Gerd Wagner - SCS Weekly Meeting

What are the Ontological Foundations of Simulation Modeling In simulation modeling, you care about modeling objects and events, since we want to simulate the real world, and these are the most important types of ontological categories in the world. Modeling and simulation (M$S) is concerned with modeling dynamical systems which considt of ojbect that are subject to state changes over tim. This happens when one or more of its attribute values are changed. These attributes that change are called state variables. Attribute values may be continuous (smooth) or discrete (in jumps), leading to continuous or discrete processes. In ISs we are typically more concerned with discrete processes. Sometimes, a mix of them: discrete events (e.g. a car bumping into another in traffic) but also continuous events (movement of different objects in traffic). Discrete Systems: - Example: predator-prey ecosystem such as an area populated by wolves and sheep, where births, death and predator-prey encounters are events. - A discrete dynamical system (such as the one in the example above) can be captured either more abstractly with the help of a continous simulation as in System Dynamics, or iwth the help of a Discrete Event Simulation model. Discrete Evnet Simulation (DES) Paradigms: - Event-based simulation with SIMSCRIPT (1962), Event Graphs (1983) - Process Network simulation with GPSS (1961), Arena (1992), AnyLogic etc. It is based on more high-level concepts w.r.t events, which help you to capture concepts of different domains (e.g. manufacturing, traffic etc.) - Coroutine-based Process Interaction simulation with Simla (1967), SimPy, etc. Coroutines are asynchronous programming process stations, which may start, be interrupted and then reestablish processing. - Simulation based on Petri Nets (from the 60s) Object Event M&S Based on the ontological principles: - objects participate in events - events cause state changes of participating objects and follow-up events according to causal regularities. The sturcture of objects and events is described in the form of a UMLclass mode defining object types and event types. The system's dynamics is described in the forms of DPMN (similar to BPMN) process model defining a set of rules. - which caputre causal regularities (as event rules) - and correspond to transition functions of an Abstract State Machine. Causal Regularity Simple Model:

Example of Object Event Model about Phishing We may see an OE Class Model to model the information and a BPMN/DPMN model to model the events

Agent-based M&S - ontologically speaking, agents are special objets that interact with each other and with their environment. - agents interact with their envionment via a perception-action cycel that is modeled in OEM&S in the form of perception events and action events. - Agents interact with each other by sending and receiving messages. In OEM%S, sending a message is an out-message action event and reeiving a message is an in-message event. Example of a basic BPMN Model of Phishing

Example of a Conceptual Information Model about Phishing

Besides the regular relationships (composition, specialization, and general associations), in these kind of Information Models, there are special kinds of associations and multiplicity restrictions: - the association between the entities mean the participation of agents/objects in events. - the multiplicity can indicate snapshot or historical multiplicity restrictions (you may need both kinds of multiplicity in one model).

Crafting Future Scenarios with the Help of AI - Roland M. Mueller, Katja Thoring at al.

Developing future research poses some problems, including the fact that you don't have the users for manymuch the technology you want to produce. Research questions: Goals: provide AI assistance to future scenario development; AI assistance with scenario rating; AI assisatance with Qualitative Feedback, AI assistance with scenario iterations etc. - Can we democratize access to collective expert knoweldge through Generative AI? - Can we expand the established - Can we build human twins to Project called: Delphi Study Experiment 1: They developed 23 future scenarios using a panel of experts: people from different non-AI fields, such as science fiction authors, business people. And they conmpared that with the ideas of the people in the AI research field. E.g. of solution of the painel of experts: Digital Detox Zone (a place in the office which is not digitally supported) Experiment 2: compare the Human expers and AI experts with a Digital Twin. In short, it does not work yet. Paper to read: Designing the Future With the “Delphi Design Sprint”: Introducing a Novel Method for Design Science Research - https://www.researchgate.net/publication/357746370_Designing_the_Future_With_the_Delphi_Design_Sprint_Introducing_a_Novel_Method_for_Design_Science_Research Discussion about the use of Digital Twins in these scenarios: - Good potential for triangulation with field experts and AI people. - Good inspiration for future works in this area Ethical concerns: - GenAI hallucinations are not asuch aproblem for scenairo development compared to factual quesitons - Tranparency of AI involvement - Specific requirement and charactiristic of AI scneaqrio Crafting Future Scnarios with the Help of AI: Potentials of a Hybrid Delphi Expert Panel. HICSS Mind th eFuturee Gap: Introducting the FOD Framework for Future Oriented Design. HICSS

Digital Everything: From Twins to Circular Economy - Barbara Dinter

Digital Everything: From Twins to Circular Economy Barbara Dinter Barbara is one of the IS chairs, focusing on Business Intelligence in TU Chemnitz This presentation is about some german-funded projects. Project 1 - Co-Twin - Vision of a collaboration digital tiwn (DT) in value chain networks. - Whole life cycle - she applies Business Models They transfered the ARIS idea of views (BP view, Data view etc.) to Digital Twins. They have: component view, data view, visualization view, network view... and others. - For all stakeholders in a value chain. The DT is used on the planning phase Results: demonstrator prottoype, 3 use cases, taxanmy, reference architecture, design guidelines and conceptualizations. Project 2 - The circular economy - Part 1 - integration with Co-Twin project Goals: sustainability, enrionmental protection and increased efficiency. Key aspects: Reduce, reuse, repair and recycle; sustainable business models, systemic approach, design for longevity and integration of digital technologies. - Part 2 - The circular economy Digital ecosytem for circular economy in the automotive industry (DIONA) collaboration with other academic partners: TU Dortmun and Fraunshofer ISST. She also mentioned 12 projects with industry. DIONA Focus areas - Transfer and networking: coordination of 12 MobilKreis projects, oraganization of physical and digital meetings, knwoeldge tranfer research activities. - Cyberphysical Lab for SMEs to open experimental space for simulations and test and vailidate scnearios without disrupting live processes. - Digital Hub Research topics: 1) conceptualization of use caes in Circular economy 2) BPM in Circular Economy (adaptation of capabilities, models and technologies for that)

Fundamentals of Disaster Management Sytems: A Computer Science Perspective - Mehmet Aksit - University of Twente - 3-7-2023

Fundamentals of Disaster Management Sytems: A Computer Science Perspective Mehmet Aksit - University of Twente - 3-7-2023 Outline: Disaster Management as it is today Problem Statement Process Automation for Disaster Management Today: 1) Disaster Prediction
2) Post-Disaster:
- Identification
- Assess
- Understand
- Cope
- Strategy
- Recovery Procedures

Problem statement He worked with the auhtorities and developed requirements for dealing with disaster and without looking at the trends, really focused on talking to authoraties' stakeholders. They used different viewpoints to elicit requirements

They used quesionnaires with wanted-not wanted 5 likert scale in a large organization in Turkey. This study identified 65 requirements that are now prioritized. For prioritization, they identified which ones could be deffered to a later time without causing technical problems during disaster management (priorization in 3 groups or requirements). After that, they synthesized to identify technologies and skills to fulfill the requirements.
Obsserved problems: they analyzed the problems and saw that it would be difficulty to deliver Process Automation in the way they intended.

They generalized the problem of process automation as a problem of Demand and Supplies. There are several related works in aid optimization for disasters. But they have some limitations: generally offline optimizations, problem-specific (case by case), product specific (for specific systems), weak or absence of automated process support, accordingy, lcak of an online control system platform to manage disasters effectively and efficiently.

Solutions for the demand and supply problem

Example: They have about 100 tables like the one in the picture below, with established rules. Then the cocneptual model on the right hand corner guides the automation of a process to deal with a particular disaster. They create tasks and group tasks that compose jobs, and this is assigned to different people.

If there isn't enough resource, then some strategies are developed (trade-off analysis, prioritization of jobs, etc.)

To sum up, for them, a disaster problem is a resource allocation problem.
Upcoming publication:

Disaster Prediction Another direction of work they do is to predict disasters. They do that based on the concept of 'Event'.
I asked them if they do that by analyzing social media, but he said 'no'. They use other data sources.
Giancarlo has asked an interesting question about causality, in other words, how to identify that an event will happen because it is caused by a previous event occurence that has been already identified. Mehmet said that they treat this in their approach.
Event specification:

Upcoming publication:

Using Digital-Twins for disaster prediction and handling:

Conclusions

From a ML point of view, there is a lot to be developed such as:

Notes on the discussion subsequent to the talk:

There are many advanced simulation systems out there, and instead of producing new ones, we should use the existing systems that are already very sophisticated.

They keep collaborations with the Disaster Center at the Univ. of Sao Carlos, in Brazil, and they hope to talk to the Federal Disaster Management team as well. The main problem that he sees is that Brazil, like in Turkey and many other countries, there is a lot of data from satelites and other state of the art sensors, but there is no data on disaster management process. And the main problems for him are process problems. That is why they are working on process automation, and these partners have been very excited about this kind of work.

It is even relatively easy to find data sets on disasters, but not on disaster management processes. Therefore, it is very important to work on producing this data, as well as integrating such data from different data sources. Also during the pandemic he noticed that most of the work was on putting data together from different systems. That is why we need International Alliances and a lot of work on data integration.

Their system is responsible to make decisions to allocate tasks to teams in disaster management. The decisions are based on rules and ML algorithms that adapt the rules for making the predictions and recommendations. How to cope with people's resistance in using such systems? Jan says that perhaps the operator should give the last word and not the system. But actually in many areas, we see systems that already take the decision on our behalf and they can do it better than us (e.g., piloting planes, flood gates in Rotterdam). Mehmet says that something important is to categorize the type of disaster - meaningful categories! (see slide on ML opportunities above). Depending on the type of disaster, there can be more or less human intervention. Giancarlo suggests that work should be done in: intentional (goal) modeling to understand what people's intention is in the middle of a disaster (e.g. Paris riots). Mehmet says: another important issue is to deal with ethics and privacy in this domain.

Unitn Class - 16-12-2019 - Seminar on AlpineBits - By Claudenir Morais Fonseca

AlpineBits
By Claudenir

Focus: create a standard for touristic events of the Alto Adige region.

The standard is based on formats such as XML/JSON

The standard is free and may be downloaded from the AlpineBits website.

It is not a service... it is a contract to use a particular format on data exchange.

Main motivation: to exchange knowledge in a common format,, so that all businesses and the government can consume this data. In the end, the businesses are competitors, but they collaborate on developing the standard, because it is advantageous for all of them.

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Scope

Touristic events: events, event series, venues, organizers etc.
Touristic areas: lifts, trails,, slopes, mountain areas etc.
Additional information: agents, multimedia, metadata etc.

-------------------------------

Eventdata reference model

*He showed several models, some were class models, other instance models. This was very interesting to illustrate the usefulness of the approach.

The more complete model contains the following concepts: event, event series, venue, venue allocation, the roles of the people and organizations connected to the event, and target audience.

--------------------------------

They reuse as much as possible. For example, they use schema.org.
Reusing is important to guaranteeing acceptance by partners (people want to use popular solutions, which they are already familiar with).
However using the things they reuse alone is not enough, because this would keep the information too vague.
So their solution builds on top of the existing artefacts/tools

-------------------------------

They applied GitLab and created issues for everything they had to do for the project.
And they followed the good practice of having a structured development, using features path, development path and master path, leaving the master path as clean as possible.

The repository is public and thus available for the students to take a look

----------------------------------

One of the most important things in Claudenir's view is testing. You must have a way to guarantee the quality of the code, especially if you expect someone else to revise your code.

Here is the testing tools Alpine Bits make available.

--------------------------------------

Some requirements
minimize message size
one way to respresent information
(...)

-----------------------------------

Main use cases

• Request-Response
• Publisher-Subscriber
• Batch exchanges

-----------------------------------

Interaction Types

• Request-Response
• Publisher-Subscriber
• Batch
• Streaming

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API Styles

• Tunnel Style - having one endpoint and everything else goes on the message
• URI Style (Partial REST)
• Hypermedia (Full REST)
• Query Style
• Event-driven Style

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In general, the resources for AlpineBits developers are found here. 

ER 2019 - Industrial Pannel - with Oscar Pastor, Karin Breitman and C. Mohan

Industrial Pannel
*My own comments are marked with asterisks

The panelists shall respond the following questions:

1. What are the main inhibitors of modelling in practice?
2. What could be done to improve the popularity of conceptual modelling in practice?
3. 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. 

ER 2019 - SCME - Conceptual Modeling Education Panel

SCME - Conceptual Modeling Education Panel 
*My comments are marked with an asterisk

Participants:

• Barbara Weber (University of St.Gallen), Switzerland
• Geert Poels (Ghent University), Belgium
• Monique Snoeck (KU Leuven) Belgium
• Matthias Jarke (RWTH-Aachen University), Germany
• Oscar Pastor (Universidad Politecnica de Valencia), Spain
• Giancarlo Guizzardi (Free University of Bozen-Bolzano), Italy – Mediator

The panel started with Giancarlo's presentation about CM Education. He raised some interesting points based on the 5W1H model, to be responded by each of the painelists:

Example:

• How to teach/learn CM?
• Who shall we teach? Not only computer science
• When? Shall we start from a young age or not?
• What shall we teach when teaching Conceptual Modeling?

1) Matthias Jarke:

• CM education should be interdisciplinary 
• CM meets technology-enhanced learning

He showed a system to support Real-time Collaborative Modeling 

At the moment, they are working on WEKIT - Wearable Exxperience for Knowledge Intensive Training. 

This is a Horizon 2020 with 12 partners. It may be useful also in the CM domain.

2) Oscar Pastor

Pros and Cons of CM-based Development in a Practical Teaching Experience

How to motivate the students in MDD?

They teach two courses on MDD. In the first course, they involve the users in an experiment that test accuracy, effort, productivity and satisfaction regardin CM. The problem is that the complexity of the problem seems to affect all variables. Then, they conducted a replication enlarging the problem complexity to check this idea in the second course. The results are much clearer in the second experiment.

In their case, there is a direct relationship between correct models and compilable code, because the code comes from the model. 

Some challenges:

- they use 4 hours for the experiment. That seems to be too much

- the size of the problem (complexity) is also tricky to find.

Answering the questions:

• How to teach/learn CM? How to change people's conceptualization capabilities? I don't know. What I suggest: practice, practice, practice.

• What shall we teach when teaching Conceptual Modeling? selecting a CM domain and a CM language; structure / behavior  / user interaction / etc.; identify the level of abstraction.

• Innovative ways of teaching: different types of exercises, not only IS/SE based exercises.

More questions:

- big difference in CM abilities among students:  some people are naturally good, others are not. Not sure how to address this.

- should a software engineer be graduated without assessing a solid CM ability? He thinks not.

3) Geert Poels

He teaches Bachelor and Master Business students

- What?

Concept is 

• anything that has existed, currently exist, will exist, could exist or cannot exist
• from very concrete to very abstract
• heuristic: if we can think of it, it is a concept

Focus on the model = properties of the concepts. In particular, relationships between the concepts.

- Why

representation - to talk about something, we must represent it

abstraction - abstract away from unneeded stuff

visualization - a picture is worth more than 1000 words

We use it for understanding, communicating, sense-making, problem analysis and solution design, i.e. for much more than only IS development! 

He presented some interesting domains in which this would be relevant, some of which are not related to IS development.

- How

Conceptual modeling - ER Diagrams (UML notation)

Business Process Modeling - BPMN

Enterprise Modeling - ArchiMate

Many of his students do high-end consultant and/or get involve in innovation projects. Most of these projects involve ISs. 

He works this question with his students: 

How to use enterprise modeling to analyse and demonstrate the impact and value o digital technology?

4) Monique Snoeck

Most of her students cannot program, so CM is the only way  they access how we can build ISs.

- What

Fundamentals of modeling - The World vs. the Machine (M. Jackson)

• She focus on this relationship world vs. machine itself
• Basic principles of Description

Modeling Quality Frameworks

• Semiotics: transformation effects (see book of K. Pohl)
• Lindland & Sindre - Sintax, Semantics, Pragmatics
• CMQF (Conceptual Modeling Quality Framework - Nelson, Poels and friends)

She teaches any language: state machines, UML or whatever as examples of languages that follow the previously mentioned principles

- How

• hands on - exercises + exercises + exercises
• apply instructional design methods - Bloom taxonomy and 4C-ID
• use smart tools to automate the teaching work (even grading)

*She presented the Bloom taxonomy and showed how it may be used. Very direct and interesting!

Every modeling task should be an authentic modeling task

you may start with simple and go to more complex tasks. For example: 

1. you may start by modeling yourself and think out loud so they follow
2. then you do step-by-step guided exercises
3. full exercise with minor hints
4. homework: full exercise

MOOC - she creates some moocs for some more simple learning objectives and leave only the more interesting/complex things for the classroom.

Flipped Classroom - video classes are prescribed; in the classroom, only questions and exercises. 

Smart Tooling: 

Feedback during modeling - she presented a tool that provides feedback while the student is modeling; 

She also uses simulation augmented with feedback to help students learn (e.g. UML class diagrams)

She pointed to Daria Bogdanova and suggested we talk to her to understand what else their group does. 

5) Barbara Weber

- What

Ensure that the students understand the concepts. In the beginning, newcomers really struggle to understand. 

She then spends a lot of time in the beginning explaining the concepts and not really go to the language.  What is a business process? What is an outcome (positive/negative outcome)? What is an event?  What is an activity? What is a decision point?

We should teach all we need to create a model:

syntax, semantics, notation, vocabulary, modeling conventions, modeling tools. 

*She presented a model structuring all these aspects in a very interesting way. 

The Process Spectrum

It should be clear to the student that there is a variety of kinds of models, and BPMN is not the ideal tool for every case. So, the students should be critical in understanding for which situation to use each modeling notation. 

- How

Novices and experts differ in their cognitive processes. 

One thing that she would like to emphasize from Monique's talk is the idea of providing feedback, even if you do it on the fly (and not supported by tools). For example, she uses a lot of exercises in class so that the students can ask questions while they do their model. 

She keeps the theory very simple and short. 

Different types of exercises:

• understanding models,
• creating models
• reviewing models
• finding errors in models 

This makes it more interesting for them, because it allows them to deal with different tasks. And moreover, they may learn different and very useful things. 

The Cheetah Platform may assist understanding the level of the student and what she is still missing. It may be used to create an Adaptive Learning Platform. 

Q&A

Book suggested by Giancarlo: 

Engineering and the Mind's Eyes - Eugene Ferguson 

- What is Scratch doing right to teach programing and what are we doing wrong? Why can't we create tools that easily teach how to do CM?

Oscar thinks that there is something more fundamental. It is easier to be a "doer" (programing) than to be a conceptual modeler (thinking why you are doing that the way you are doing)
Monique adds that the reward that the kid has when using scratch is immediately understanding if it went well or not. The simulation tool she uses does the same for CM and the students love it!