Abstract

The tutorial will introduce a new concept - discovery power - that can be used to characterize an enterprise modeling language. The concept is di erent from, but connected to, the concept of expressive power. The concept is de ned as "the degree of help provided by the structure of an enterprise modeling language to expand a partly built model or ll gaps in it". The concept is realized by discovery rules that are di erent for di erent modeling languages.

Keywords

• eol>Modeling languages
• Discovery power
• Discovery rule 1

1. Introduction

2. How it Works

To explain how the discovery rules works, we will use our own version of the OODA (ObserveOrient-Decide-Act) loop suggested by Boyd [ 4 ], the first two parts of which are depicted in Fig. 1, The central point of the OODA loop is Orient – an activity that allows one to get a clear view of the situation, which is then used to generate a hypothesis for action (Decide). The orientation in the Orient phase is done based on the information from the Observe phase obtained under the guidance and control from the Orient phase.

Feed forward from the Observe phase to the Orient phase is information based on which an enterprise model is being built. Feed forward from Orient to Decide is an enterprise model based on which a decision on actions is taken. In general, the model should include information about the environment of the enterprise in focus, e.g., competitors, and about the enterprise itself, e.g., its capabilities. The information needed to build the model can be obtained from different sources, including documents describing the environment or the organization itself, interviews with stakeholders, facilitating workshops with them, and actual observations of what is happening outside or inside the organization.

Gathering new information in the Observe phase is guided and controlled partly implicitly partly explicitly. Explicit guidance and control can be in the form of questions for interviews, instructions on what speci c information is missing and needs to be obtained, etc. This control is partly de ned by the experience and background of the modelers, which is depicted in Fig. 1 as Previous experience, Cultural Heritage and Genetic Heritage. The other part is provided by the structure of the modeling language that is used to build the model. This structure de nes which entities and relations should be found in the environment and organization. For example, based on this structure, speci c interview questions can be asked, or gaps in the model being built initiate looking for certain kinds of information in the documents.

3. Examples of Discovery Rules

Paper [ 1 ] suggests a way to operationalize the discovery power of a language so that it can be used in practice. It is done by having a set of rules that show what can be added to the model in case some speci c elements already exist in it. We will be using the rule format suggested in paper [ 2 ]. This format presumes that the language elements consist of shapes and connections between them, which are shown by lines of a certain form. An example of such a rule for the ArchiMate language [ 5 ] is presented in Fig. 2. This rule consists of two parts. One that shows what is already in the model, and one that shows what could be added to the model based on additional investigation. The elements that are added are highlighted. Added shapes have a rose background, while added connections are highlighted by having thicker lines. The rule presented in Fig. 2 means that for the actor of a business service supported by an application component, there, most probably, should exist an interface via which this actor can work with the application component. Note, however, that this needs to be established for each actor separately, because some actors may not use this application component. Also, two di erent actors can use the same interface. It can be a sign that the system is not properly designed.

If the tool for drawing models does not have a feature that allows for having thicker relations, the two-part rule de nition can be used, as shown in Fig. 3, which expresses the same rule as in Fig. 2.

In Fig. 4, we present another discovery rule, this time for Fractal Enterprise Model (FEM) [ 3 ]. It uses the same format as in Fig. 1. It shows that for the recruiting process, there needs to be some attraction that will appeal to people to become employees. It can be a salary, a modern building in the center of the town, etc. The rule says that if you are recruiting people for a position in your company, you need to have something that attracts them.

In Fig. 5, we present an example of a discovery rule for IDEF0 [ 6 ]. Suppose the output of a functional element has been established. In that case, the next step is to find where it goes, thus finding another functional element that consumes the output as input, control, or mechanism. This rule is presented in the same way as the rule in Fig. 3, i.e, as consisting of two parts.

Note that the labels in the shapes are abstract; they do not need to be matched by the labels in the actual, partly built model when the rule is applied. The matching should be done based on the shapes of the elements and connections between them, including labels on the connections.

The objective is that the attendees will learn to use the concept of discovery power by working on discovery rules for a modeling language of their choice. The intention is that the attendees will continue working on discovery rules for their preferred language and will use them in their teaching or modeling practice. One possibility is that the tutorial results in a common research paper with examples of discovery rules for di erent modeling languages.

5. Tutorial plan

The rst part (30 min) will be the introduction to the concept of discovery power, and how it can be practically realized in a number of discovery rules. In the second part (30 min), the participants will form groups according to the preferred modeling language, and they will create some discovery rules for this language. The tutorial presenter will go around groups to answer questions and, if needed, provide help. The tutorial will be nished by each group presents at least one discovery rule for their modeling language.

Acknowledgments. The work of the author was partly supported by the Estonian Research Council (grant PRG1226).

Declaration on Generative AI

The author has not employed any Generative AI tools.

References

[1] Bider , I. , Perjons , E.: On the Concept of Discovery Power of Enterprise Modeling Lan-guages and its Relation to their Expressive Power . In Malinova Mandelburger, M. , Guer-reiro, S., eds. : Advances in Enterprise Engineering XVII. EDEWC 2023, LNBIP , vol. 510 , Springer ( 2024 ) pp. 92 - 106 .

[2] Bider , I. , Perjons , E.: Discovery Rules for Depicting Tacit Knowledge Usage and Manage-ment in Fractal Enterprise Models . In: Řepa, V. , Matulevičius , R. , Laurenzi , E : (eds.) Per-spectives in Business Informatics Research , LNBIP, vol. 529 ( 2024 ), pp. 209 - 224 . Springer.

[3] Bider , I. , Perjons , E. , Klyukina , V. : Tool Support for Fractal Enterprise Modeling . In : Do-mainSpeci c Conceptual Modeling. Springer ( 2022 ) 205 - 229 .

[4] Boyd , J.: The Essence of Winning and Losing . In: Unpublished lecture notes , 12 ( 23 ), pp. 123 - 125 . (Accessed May 2025 ) Available at: http://www.expertsecho.com/johnboyd/johnboyd_docs/Essence_of_Winning_and_Losing.pdf

[5] The open group: ArchiMate® 3 . 2 Speci cation . In: The Open Group. Available at: https://publications.opengroup.org/standards/archimate/speci cations/c226, ( Accessed 2025 ).

[6] 10 . NIST: Integration de nition for function modeling (IDEF0) , Dra Federal Information Processing Standards, Publication 183 , 1993 . In: IDEF. Available at: https://www.idef.com/idefo-function_modeling_method/, (Accessed 2025 ) .