Performing a literature review is a critical first step in research to understanding the state-of-the-art and identifying gaps and challenges in the field. A systematic literature review is a method which sets out a series of steps to methodically organize the review. In this paper, we present a guide designed for researchers and in particular early-stage researchers in the computer-science field. The contribution of the article is the following: • Clearly defined strategies to follow for a systematic literature review in computer science research, and • Algorithmic method to tackle a systematic literature review.

Smart Product–Service Systems (S-PSS) represent an innovative business model that integrates intelligent products with advanced digital capabilities and corresponding e-services. The user experience (UX) within an S-PSS is heavily influenced by the customization of services and customer empowerment. However, conventional UX analysis primarily focuses on the design stage and may not adequately respond to the evolving user needs during the usage stage and how to exploit the data surrounding the use of S-PSS. To overcome these limitations, this article introduces a practical framework for developing Adaptive User Interfaces within S-PSS. This framework integrates ontologies and Context-aware recommendation systems, with user interactions serving as the primary data source, facilitating the development of adaptive user interfaces. One of the main contributions of this work lies on the integration of various components to achieve the creation of Adaptive User Interfaces for digital services. A case study of a smart device app is presented, to demonstrate the practical implementation of the framework, with a hands-on development approach, considering technological aspects and utilizing appropriate tools. The results of the evaluation of the recommendation engine show that using a context-aware approach improves the precision of recommendations. Furthermore, pragmatic aspects of UX, such as usefulness and system efficiency, are evaluated with participants with an overall positive impact on the use of the smart device.

Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components - Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented.

This article introduces a dataset of human-machine interactions collected in a controlled and structured manner. The aim of this dataset is to provide insights into user behavior and support the development of adaptive Human-Machine Interfaces (HMIs). The dataset was generated using a custom-built application that leverages formally defined User Interfaces (UIs). The resulting interactions underwent processing and analysis to create a suitable dataset for professionals and data analysts interested in user interface adaptations. The data processing stage involved cleaning the data, ensuring its consistency and completeness. A data profiling analysis was conducted for checking the consistency of elements in the interaction sequences. Furthermore, for the benefit of researchers, the code used for data collection, data profiling, and usage notes on creating adaptive user interfaces are made available. These resources offer valuable support to those interested in exploring and utilizing the dataset for their research and development efforts in the field of human-machine interfaces.

Human-machine interfaces (HMI) facilitate communication between humans and machines, and their importance has increased in modern technology. However, traditional HMIs are often static and do not adapt to individual user preferences or behavior. Adaptive User Interfaces (AUIs) have become increasingly important in providing personalized user experiences. Machine learning techniques have gained traction in User Experience (UX) research to provide smart adaptations that can reduce user cognitive load. This paper presents an ongoing exploration of a method for generating adaptive user interfaces by analyzing user interactions and contextual data. It also provides an illustrative example using Markov chains to predict the next step for users interacting with an app for an industrial mixing machine. Furthermore, the paper conducts an offline evaluation of the approach, focusing on the precision of the recommendations. The study emphasizes the importance of incorporating user interactions and contextual data into the design of adaptive HMIs, while acknowledging the existing challenges and potential benefits.