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The COVID-19 pandemic has shown companies that their on-premise infrastructures often reach their limits with a large number of remote accesses. The transition to cloud-based solutions could represent a more efficient alternative. However, many German companies, especially small and medium-sized enterprises (SME), are still hesitant to take this big step of transferring applications to the cloud. For this reason, this paper examines the question of whether existing migration approaches in the analysis phase fit the specific requirements of SMEs. Using a literature review methodology, we first identify and analyze determinant factors for cloud adoption in SMEs. On this basis, we analyze existing methods in the analysis phase for migrations from on-premise software to cloud solutions. We investigate whether these factors are considered in the analysis phase of the approaches and conclude their suitability for SMEs. Of the migration approaches we examined, none included all the factors we identified as relevant to SMEs. Fewer have considered all factors fully and in detail. We present the results of the literature search process in tabular form and conclude this paper with a discussion and synthesis of the literature as well as an outlook on further research fields.
Cloud-Computing bietet für Unternehmen große Potenziale hinsichtlich Arbeitseffizienz, Flexibilität und der Realisierung neuer Produkte und Geschäftsmodelle gegenüber einer klassischen On-Premises-IT Infrastruktur. Verschiedene technische und nicht-technische Herausforderungen hindern Unternehmen heute noch oft an den notwendigen Schritten zur Cloud-Transformation.
Das FIR an der RWTH Aachen unterstützt bei der systematischen und unternehmensgerechten Realisierung der Cloud-Potenziale mit einem dreiphasigen Konzept, angefangen bei der Zieldefinition, bis hin zur Implementierung. In den einzelnen Phasen wird eine Vielzahl bewährter Einzelmethoden in einer Gesamtmethodik zusammengeführt und so ein anwendungsfallspezifisches, auf die individuelle Unternehmenssituation zugeschnittenes Vorgehen verfolgt.
Methods of machine learning (ML) are notoriously difficult for enterprises to employ productively. Data science is not a core skill of most companies, and acquiring external talent is expensive. Automated machine learning (Auto-ML) aims to alleviate this, democratising machine learning by introducing elements such as low-code / no-code functionalities into its model creation process. Multiple applications are possible for Auto-ML, such as Natural Language Processing (NLP), predictive modelling and optimization. However, employing Auto-ML still proves difficult for companies due to the dynamic vendor market: The solutions vary in scope and functionality while providers do little to delineate their offerings from related solutions like industrial IoT-Platforms. Additionally, the current research on Auto-ML focuses on mathematical optimization of the underlying algorithms, with diminishing returns for end users. The aim of this paper is to provide an overview over available, user-friendly ML technology through a descriptive model of the functions of current Auto-ML solutions. The model was created based on case studies of available solutions and an analysis of relevant literature. This method yielded a comprehensive function tree for Auto-ML solutions along with a methodology to update the descriptive model in case the dynamic provider market changes. Thus, the paper catalyses the use of ML in companies by providing companies and stakeholders with a framework to assess the functional scope of Auto-ML solutions.