Keywords: Process Mining, Multi-Modal Segmentation, Conceptual Modeling

Keywords: AI, Enterprise Architecture, Enterprise Modelling
Astract: A future enterprise will be a complex ecosystem (or system of systems) that operates in a dynamic uncertain environment. It will need to continue delivering the stated goals while dealing with unforeseen changes along multiple dimensions such as events opening up new opportunities or constraining the existing ones, competitor actions, regulatory regime, law of the land and technology advance/obsolescence. Customers increasingly demand highly personalized services and user experiences that can change depending on market trends. The goals that drive the operation of an enterprise can change over time. Businesses are increasingly regulated. Given the increased dynamics, existing regulations will keep changing frequently, and new regulations will get introduced at a faster rate. Responsive compliance management with minimal exposure to risk will therefore be a universal key requirement that will be felt increasingly acutely across business domains. Increasingly, enterprises are pledging to the sustainable development goals proposed by the UN. Quite a few domains are witnessing stiff competition from new entrants such as FinTech companies in banking. Enterprises need to significantly reduce the costs to continue to be viable in the face of this technology-centric agile competition. Moreover, as the Covid-19 pandemic has revealed, enterprises need to be prepared to quickly adapt in the face of black swan events.

Keywords: Enterprise Modelling
Astract: In the Western world, digital has become the new normal, both in our daily lives and at our work. Additionally, Western countries have seen a transition from a goods-oriented economy to a services-oriented economy. Whereas, in the recent past, it was already the case that change was the only constant, these intertwined, and mutually amplifying, trends even further increase the pace of change. As a result, enterprises are confronted with a need to transform (continuously) accordingly. During any enterprise transformation, coordination among the key stakeholders and the projects that drive the transformations is essential. A shared understanding, agreement, and commitment are needed on (1) what the overall mission/vision of the enterprise is, (2) the current affairs of the enterprise and any ongoing changes, (3) the current affairs of the context of the enterprise, and (4) what (given the latter) the ideal future affairs of the enterprise are. Models, and ultimately enterprise (architecture) modeling languages and framework, are generally considered as an effective way to support such (informed) coordination. In the past, different frameworks and languages have been developed to this end, including the ArchiMate language. The latter has evolved to become a widely accepted industry standard. The objective of this chapter is threefold: (1) we intend to illustrate some of the key challenges which the digital transformation, and the two intertwined trends that drive it, puts on enterprise (architecture) modeling languages, (2) assess to what extent ArchiMate meets these challenges, and (3) draft the outline of a next-generation enterprise (architecture) modeling language (framework) that may be more suited to meet the challenges of these trends.

Keywords: Developing Economies, e-Government, e-Government Interoperability, Enterprise Architecture
Astract: The growth and uptake of e-government in developing economies is still affected by the interoperability challenge, which can be perceived as an orchestration of several issues that imply the existence of gaps in methods used for e-government planning and implementation. To a great extent, various counterparts in developed economies have succeeded in addressing the method-related gaps by developing e-government enterprise architectures, as blueprints for guiding e-government initiatives in a holistic and manageable way. However, existing e-government enterprise architectures are country-specific to appropriately serve their intended purpose, while enterprise architecture frameworks or methods are generic to accommodate several enterprise contexts. The latter do not directly accommodate the unique peculiarities of e-government efforts. Thus, a detailed method is lacking that can be adapted by developing economies to develop e-government enterprise architectures that fit their contexts. To address the gap, this article presents research that adopted a Design Science approach to develop an e-Government Enterprise Architecture Framework (EGEAF), as an explicit method for guiding the design of e-government enterprise architectures in a developing economy. EGEAF was designed by extending the Architecture Development Method of The Open Group Architecture Framework (TOGAF ADM) to address requirements for developing interoperable e-government solutions in a developing economy. EGEAF was evaluated using two scenarios in the Ugandan context, and findings indicate that it is feasible; its design is understandable to enable its adoption and extension to accommodate requirements for developing interoperable e-government solutions in other developing economies.

Keywords: Conceptual Modeling, Analytical models, Systematics, Machine Learning algorithms, Bibliographies, Semantics, Machine Learning
Astract: Conceptual models are essential in Software and Information Systems Engineering to meet many purposes since they explicitly represent the subject domains. Machine Learning (ML) approaches have recently been used in conceptual modeling to realize, among others, intelligent modeling assistance, model transformation, and metamodel classification. These works en-code models in various ways, making the encoded models suitable for applying ML algorithms. The encodings capture the models' structure and/or semantics, making this information available to the ML model during training. Therefore, the choice of the encoding for any ML-driven task is crucial for the ML model to learn the relevant contextual information. In this paper, we report findings from a systematic literature review which yields insights into the current research in machine learning for conceptual modeling (ML4CM). The review focuses on the various encodings used in existing ML4CM solutions and provides insights into i) which are the information sources, ii) how is the conceptual model's structure and/or semantics encoded, iii) why is the model encoded, i.e., for which conceptual modeling task and, iv) which ML algorithms are applied. The results aim to structure the state of the art in encoding conceptual models for ML.