AI systems cannot exist without data. Now that AI models (data science and AI) have matured and are readily available to apply in practice, most organizations struggle with the data infrastructure to do so. There is a growing need for data engineers that know how to prepare data for AI systems or that can setup enterprise-wide data architectures for analytical projects. But until now, the data engineering part of AI engineering has not been getting much attention, in favor of discussing the modeling part. In this paper we aim to change this by perform a mapping study on data engineering for AI systems, i.e., AI data engineering. We found 25 relevant papers between January 2019 and June 2023, explaining AI data engineering activities. We identify which life cycle phases are covered, which technical solutions or architectures are proposed and which lessons learned are presented. We end by an overall discussion of the papers with implications for practitioners and researchers. This paper creates an overview of the body of knowledge on data engineering for AI. This overview is useful for practitioners to identify solutions and best practices as well as for researchers to identify gaps.

Hub71 and e& enterprise, part of e& (formerly known as Etisalat Group), have launched the region's first AI Center of Excellence (AI CoE) in Abu Dhabi. The AI CoE will provide a platform for AI solutions to be built and scaled from Abu Dhabi. By offering resources and expertise, the center is said to transform the future of AI, support a thriving innovation ecosystem, foster local talent, and boost the country's socio-economic growth. The partnership was signed at Hub71's headquarters in Abu Dhabi by Badr Al-Olama, acting chief executive officer of Hub71, and Salvador Anglada, chief executive officer of e& enterprise. Al-Olama said: "The region's first AI Center of Excellence at Hub71 will provide a robust ecosystem for innovative technology ideas to grow and scale, and will allow startups to benefit from Hub71's community, programmes and knowledge sharing platforms." Today, we signed an agreement with #eandEnterprise to develop #AI by launching the first Artificial Intelligence Center of Excellence in the region to create a smarter & safer sustainable world through the co-creation of industry-specific and use case-driven (Al) solutions.

With increasing linkage within value chains, the IT systems of different companies are also being connected with each other. This enables the integration of services within the movement of Industry 4.0 in order to improve the quality and performance of the processes. Enterprise architecture models form the basis for this with a better buisness IT-alignment. However, the heterogeneity of the modeling frameworks and description languages makes a concatenation considerably difficult, especially differences in syntax, semantic and relations. Therefore, this paper presents a transformation engine to convert enterprise architecture models between several languages. We developed the first generic translation approach that is free of specific meta-modeling, which is flexible adaptable to arbitrary modeling languages. The transformation process is defined by various pattern matching techniques using a rule-based description language. It uses set theory and first-order logic for an intuitive description as a basis. The concept is practical evaluated using an example in the area of a large German IT-service provider. Anyhow, the approach is applicable between a wide range of enterprise architecture frameworks.