Working on a larger project from conception to practical implementation, individually or in a group. Students improve their communication and presentation skills. They learn to develop new methods based on familiar ones and to assess the consequences of professional decisions.

During the course of this project students will realize a practical assignment beginning with a coarse-grained requirements definition culminating in a final prototypical realization. The exact definition of your project assignment will be done together with your advisor and depends on the chosen topic, the topic's complexity, and the size of your team (if you are working in one).

Important information: 

The 194.152 Enterprise & Process Engineering (6ec) course is a follow up to the courses 194.043 Enterprise Architecture (3ec) and 188.924 Workflow Modeling & Process Management (3ec). The latter two courses will not be offered anymore.

However, as a transitional measure for 2023W and 2024W, students who already passed one of these two latter courses successfully do not have to attend the full 194.152 Enterprise & Process Engineering course. To enable this, the course (for 2023W and 2024W) is actually divided into two modules: BPM and EA.
Students need to:
(1) register for the 194.152 Enterprise & Process Engineering (6ec) course,
(2) in TUWEL identify which of the two modules you plan to attend (BPM & EA, only BPM or only EA) in TUWEL.

For students who only attend one of the two modules, the final grade for 194.152 will be the average of the final grade received for the course that was already attended in the past (188.924 or 194.043) and the final grade of the newly attended part in the context of 194.152.
For students who attend both modules, the final grade will be based on the total number of points earned across the two modules.

• Enterprise architecture methods and frameworks.
• Enterprise architecture languages.
• Analysis of an enterprise architecture.
• Business process management methods and frameworks.
• Business process modelling languages.
• Analysis and improvement of business processes

Sustainability is not an optional feature. Targeting economic, social, and ecological issues through sustainable development has become a must, not least since the publication of the UN’s Sustainable Development Goals in 2015.

Computer Science (CS), and more prominently and recently, Artificial Intelligence (AI), is hailed as the solution to many sustainability problems (and yes, it is!). Still, energy efficiency and CO2 reduction must be considered to ensure fair and ecological use of resources. Hence, dealing with CS and sustainability involves two faces of the same coin: Sustainability with CS, often referred to as “Computational Sustainability”, and Sustainability for CS, commonly known as “Sustainable CS”.

After a successful first round in the winter term 23/24, we start into the second round. This lecture series is unique in Austria, with all Austrian CS departments and faculties participating. We want to raise awareness for the utmost importance of sustainability in CS, demonstrating research solutions to various problems, and triggering developments for a sustainable future.

The topics of the lecture are among others

• Introduction in IT Security/ Basics of IT Security 
• Approach and Point of View of Attackers 
• Methods of Attackers to Gain Information 
• Risk Analysis 
• Access Control (Authentication, Authorization,...) 
• Operating System Security 
• Network Security 
• Basics of Cryptography 
• Security in Software Development 
• Security of Applications 
• Security of Web Applications 
• Organizational Security 
• Discovery of Security Problems 
• Security Tests 

Lecture 1: Introduction

This lecture discusses: (i) the importance of conceptual modeling to several areas in computer science; (ii) the relation between conceptual modeling, ontology, and real- world semantics, and their relation to the problem of Semantic Interoperability (SI).

Learning goals: after this lecture you should be able to

• Explain the course learning methodology, goals, activities
• Explain the term Ontology in its multiple interpretations
• Explain what is semantic interoperability and how ontologies (and Ontology) can be used to support it

Materials

1. Guizzardi, G., On Ontology, ontologies, Conceptualizations, Modeling Languages, and (Meta)Models, Frontiers in Artificial Intelligence and Applications, Databases and Information Systems IV, Olegas Vasilecas, Johan Edler, Albertas Caplinskas (Editors), ISBN 978-1-58603-640-8, IOS Press, Amsterdam, 2007.
2. Guarino, N., Guizzardi, G., Mylopoulos, J., On the Philosophical Foundations of Conceptual Models, Frontiers in Artificial Intelligence and Applications, Information Modelling and Knowledge Base, Vol. 31, Selected Revised Papers of the 29th International Conference on Information Modeling and Knowledge  Bases  (EJC’19),  Lappeenranta,  Finland,  2020. DOI: 10.3233/FAIA200002
3. Guizzardi, G., Ontology, ontologies and the ‘I’ of FAIR, Data Intelligence, MIT Press, Volume 2, Issue 1-2, p.181-191, 2020. DOI: 10.1162/dint_a_00040

Lecture 2: Types and Taxonomic Structures

Types are fundamental for conceptual modeling and knowledge representation, being an essential construct in all major modeling languages in these fields. Despite that, there has been a lack of theoretical support for precisely defining a consensual view on types. As a consequence, there has been a lack of precise methodological support for users when choosing the best way to model general terms representing types that appear in a domain, and for building sound taxonomic structures involving them. This lecture presents: (i) the theory of types put forth by the foundational ontology UFO, which has been specially designed to address these issues; (ii) a fragment of the OntoUML language - comprising a set of constructs and constraints (grammatical rules) – that has been designed following this theory.

Learning goals: after this lecture you should be able to

• Explain the ontological distinctions among types according to UFO and their formal meta-properties
• Explain the OntoUML primitives reflecting these distinctions and the modeling rules on how they can be combined
• Apply this fragment of OntoUML to design conceptual models containing ontologically consistent taxonomic structures
• Critically Evaluate and Rectify the ontological consistency of taxonomic structures present in conceptual models

Materials

1. Guizzardi, G., Fonseca, C., Almeida, J.P., Sales, T.P., Benevides, A.B., Porello, D., Types and Taxonomic Structures in Conceptual Modeling: A Novel Ontological Theory and Engineering Support, Data & Knowledge Engineering, Elsevier, 2021. DOI: 10.1016/j.datak.2021.101891.
2. Batista, J.O., Almeida, J.P., Zambom, E., Guizzardi, G., Ontologically correct Taxonomies by construction, Data & Knowledge Engineering, Elsevier, 2022. DOI: 10.1016/j.datak.2022.102012.

Lecture 3: Modes, Relators and Relations

In this lecture, we will continue with the study of Aspects. Firstly, we will review the notions of qualities and their associated quality spaces (e.g., color, weight, electric charge), and demonstrate how they are modeled in terms of (Structured) Datatypes and associated constraints in OntoUML. Secondly, we will study another type of intrinsic aspects, namely, the notion of Modes. In particular, we will investigate the notion of relational or externally dependent modes. Furthermore, we will study the category of relators as aspects that are existentially dependent on multiple individuals. Finally, we will present a number of ontological distinctions among the categories of relations, demonstrating how a particular class of relations termed material relations are grounded on concrete entities (truthmakers), typically relators.

Learning goals: after this lecture you should be able to

• Explain the categories of Qualities, Modes and Relators
• Explain the ontological distinctions among relations according to UFO and their formal meta-properties
• Explain the OntoUML primitives reflecting the distinctions above and the modeling rules on how they can be combined
• Apply this fragment of OntoUML to design conceptual models containing ontologically consistent taxonomic structures
• Critically Evaluate and Rectify the ontological consistency of representations of qualities, models, relators and relations in conceptual models

Materials

1. Guizzardi, G.; Masolo, C.; Borgo, S., In the Defense of a Trope-Based Ontology for Conceptual Modeling: An Example with the Foundations of Attributes, Weak Entities and Datatypes, 25th International Conference on Conceptual Modeling (ER’2006), Arizona, USA, 2006. DOI: 10.1007/11901181_10.

Lecture 4: Relations, Qua Entities and Ontological Patterns

In this lecture, we will demonstrate that the typology of types we study in Lecture 3 - as well as the catalog of Ontology Design Patterns emerging from it - can be used to differentiate and compose taxonomic structures for all Endurant Types, not only Object Types. In other words, kinds, subkinds, phases, roles, rolemixins, etc. can also be applied to model Aspect (i.e., Qualities, Modes, Relator) Types. Moreover, we will look inside the truthmakers of material relations to expose their structure showing that typically they are relators composed of particular types of externally dependent modes called Qua Entities. The explicit modeling of the latter can then address some important modeling problems, including the problem of incompatible attributions to the same entity and the Counting Problem. Furthermore, we will see how recognizing the relation between the truthmakers of different material relations can help us to identity and model relations between these relations themselves.

Learning goals: after this lecture you should be able to

• Explain how the typology of types in UFO and the Design Patterns emerging from it can be used to model all types of Aspect Types
• Apply the representation of this typology and associated Design Patterns in OntoUML to design ontologically consistent taxonomic structures among other Aspect Types
• Critically Evaluate the use of these Design Patterns in OntoUML models
• Explain the different truthmakers of material relations. In particular, explain the relation between Relators and Qua Entities, and how the relation between these truthmakers can explain the different relations of subsetting, specialization, redefinition between material relations
• Apply the fragment of OntoUML representing these entities to design ontologically consistent conceptual models

Materials

1. Guarino, N., Guizzardi, G., “We need to Discuss the Relationship”: Revisiting Relationships as Modeling Constructs, 27th International Conference on Advanced Information Systems Engineering (CAISE 2015), Stockholm, 2015.
2. Fonseca, C., Porello, D., Guizzardi, G., Almeida, J.P., Guarino, N., Relations in Ontology-Driven Conceptual Modeling, 38th International Conference on Conceptual Modeling (ER 2019), Salvador, Brazil.
3. Guarino, N., Sales, T.P., Guizzardi, G., Reification and Truthmaking Patterns, 37th International Conference on Conceptual Modeling (ER 2018), Xi’an, China.
4. Ruy, F., et al., From Reference Ontologies to Ontology Patterns and Back, Data & Knowledge Engineering, Elsevier, 2017.
5. Guarino, N., Sales, T.P., Guizzardi, G., Reification and Truthmaking Patterns, 37th International Conference on Conceptual Modeling (ER 2018), Xi’an, China.
6. Guizzardi, G., Agent Roles, Qua Individuals and The Counting Problem, Invited Chapter in Software Engineering of Multi-Agent Systems, vol. IV, P. Giorgini, A.Garcia, C. Lucena, R. Choren (eds.), Springer-Verlag, 2006.
7. Costal, D., et al., Formal Semantics and Ontological Analysis for Understanding Subsetting, Specialization and Redefinition of Associations in UML, 30th International Conference on Conceptual Modeling (ER 2011), Brussels, Belgium, 2011.

Lecture 5: Complexity Management, Model Validation, and the Road Ahead

In this final lecture, we will see a number of engineering operations that can be done with OntoUML models, and which are only made possible by the rich semantics of this language. We will begin by discussing the problem of complexity management (CM) of large-scale conceptual models. In particular, we will discuss the CM operations of modularization and abstraction, and show how we can leverage on the ontological distinctions behind OntoUML to fully automate these operations. Secondly, we will discuss the problem of model validation. Connected to that, we will also characterize the notion of ontological anti-patterns. We will then investigate an approach for model validation via visual simulation, as well as an approach for automating anti-pattern detection and elimination. Regarding the latter, we will discuss the role of domain constraints for anti-pattern elimination. Finally, we will conclude the course with a final brief review of the material presented herein and a discussion of what lies ahead in an advanced course in Ontology-Driven Conceptual Modeling.

Learning goals: after this lecture you should be able to

• Explain the importance of complexity management in large-scale conceptual models and, in particular, the difference between modularization and abstraction
• Apply the OntoUML rules for modularization and abstraction in conceptual models represented in that language
• Explain the role of model validation, as well as an approach for doing that via visual simulation and anti-pattern detection and correction
• Critically Evaluate models for the presence of a few classical anti-patterns
• Apply domain constraints to remove these anti-patterns
• Explain how an advance course in Ontology-Driven Conceptual Modeling could complement this one

Materials

1. Guizzardi, G., Sales, T.P., Almeida, J.P.A., Poels, G., Automated Conceptual model Clustering: A Relator-Centric Approach, Software and Systems Modeling (SoSyM), 2021.
2. Guizzardi, G., Figueredo, G., Hedblom, M., Poels, G., Ontology-Based Model Abstraction, IEEE 13th International Conference on Research Challenges in Information Science (RCIS 2019), Brussels, Belgium, 2019.
3. Sales. T.P., Guizzardi, G., Ontological Anti-Patterns: Empirically Uncovered Error-Prone Structures in Ontology-Driven Conceptual Models, Data & Knowledge Engineering, Elsevier, 2015.
4. Guizzardi, G., Ontological Patterns, Anti-Patterns and Pattern Languages for Next-Generation Conceptual Modeling, invited companion paper to the Keynote Speech delivered at the 33rd International Conference on Conceptual Modeling (ER 2014), Atlanta, USA.