The increasing availability of vast quantities of data from various sources significantly impacts the insurance industry, although this industry has always been data driven. It accelerates manual processes and enables new products or business models. On the other hand, it also burdens insurance analysts and other users that need to cope with this development parallel to other global changes. A novel information system (IS) for artificial intelligence (AI)-supported big data analysis, introduced within this paper, shall help to overcome user overload and to empower human data analysts in the insurance industry. The IS research’s focus lies neither in novel algorithms nor datasets but in concepts that combine AI and big data analysis for synergies, such as usability enhancements. For this purpose, this paper systematically designs and implements an AI2VIS4BigData reference model to help information systems conform to automatically detect anomalies and increase its users’ confidence and efficiency. Practical relevance is assured by an interview with an insurance analyst to verify the demand for the developed system and derive all requirements from two insurance industry user stories. A core contribution is the introduction of the IS. Another significant contribution is an extension of the AI2VIS4BigData service-based architecture and user interface (UI) concept on AI and machine learning (ML)-based user empowerment and data transformation. The implemented prototype was applied to synthetic data to enable the evaluation of the system. The quantitative and qualitative evaluations confirm the system’s usability and applicability to the insurance domain yet reveal the need for improvements toward bigger quantities of data and further evaluations with a more extensive user group.

Artificial Intelligence (AI) and Big Data Analysis are both trending research areas that can be applied to any application domain to increase efficiency, quality, or bridge supply shortages. An example is the analysis of medical information in health informatics and its subdomains bioinformatics and medical informatics. It aims to explore medical information like, e.g., patient data or clinical studies in order to support medical doctors, healthcare professionals, and ultimately all patients. The increasing level of digitization of people's lives and global pandemics boost the relevance of this research area even further. This paper analyzes a medical information use case in order to extend and improve an existing Information System for AI-based Visual Big Data Analysis with a special focus on user empowerment. In more detail, this paper extends a software architecture, introduces a reference implementation, and refines the underlying reference model AI2VIS4BigData. It focuses on supporting both, expert users with profound technical knowledge and end users without that expertise. The resulting concepts and implementations are consequently evaluated with both target groups: A cognitive walkthrough with a group of AI and Big Data Analysis experts confirms the system's usability and a survey with medical doctors and health domain experts (potential end users) positively evaluates the developed concepts.

Climate change means coping directly or indirectly with extreme weather conditions for everybody. Therefore, analyzing meteorological data to create precise models is gaining more importance and might become inevitable. Meteorologists have extensive domain knowledge about meteorological data yet lack practical data analysis skills. This paper presents a method to bridge this gap by empowering the data knowledge carriers to analyze the data. The proposed system utilizes symbolic AI, a knowledge base created by experts, and a recommendation expert system to offer suiting data analysis methods or data pre-processing to meteorologists. This paper systematically analyzes the target user group of meteorologists and practical use cases to arrive at a conceptual and technical system design implemented in the CAMeRI prototype. The concepts in this paper are aligned with the AI2VIS4BigData Reference Model and comprise a novel first-order logic knowledge base that represents analysis methods and related pre-processings. The prototype implementation was qualitatively and quantitatively evaluated. This evaluation included recommendation validation for real-world data, a cognitive walkthrough, and measuring computation timings of the different system components.

This article focuses on assisting medical professionals in analyzing domain-specific texts and selecting and comparing Named Entity Recognition (NER) frameworks. It details the development and evaluation of a system that utilizes a generic approach alongside the structured Nunamaker methodology. This system empowers medical professionals to train, evaluate, and compare NER models across diverse frameworks, such as Stanford CoreNLP, spaCy, and Hugging Face Transformers, independent of their specific implementations. Additionally, it introduces a concept for modeling a general training and evaluation process. Finally, experiments using various ontologies from the CRAFT corpus are conducted to assess the effectiveness of the current prototype.

This paper presents a cloud-based system that builds upon the FIT4NER framework to support medical experts in training machine learning models for named entity recognition (NER) using Microsoft Azure. The system is designed to simplify complex cloud configurations while providing an intuitive interface for managing and converting large-scale training and evaluation datasets across formats such as PDF, DOCX, TXT, BioC, spaCyJSON, and CoNLL-2003. It also enables the configuration of transformer-based spaCy pipelines and orchestrates Azure cloud services for scalable and efficient NER model training. Following the structured Nunamaker research methodology, the paper introduces the research context, surveys the state of the art, and highlights key challenges faced by medical professionals in cloud-based NER. It then details the modeling, implementation, and integration of the system. Evaluation results—both qualitative and quantitative—demonstrate enhanced usability, scalability, and accessibility for non-technical users in medical domains. The paper concludes with insights gained and outlines directions for future work.