This paper introduces an approach to develop an up-to-date reference model that can support advanced visual user interfaces for distributed Big Data Analysis in virtual labs to be used in e-Science, industrial research, and Data Science education. The paper introduces and motivates the current situation in this application area as a basis for a corresponding problem statement that is utilized to derive goals and objectives of the approach. Furthermore, the relevant state-of-the-art is revisited and remaining challenges are identified. An exemplar set of use cases, corresponding user stereotypes as well as a conceptual design model to address these challenges are introduced. A corresponding architectural system model is suggested as a conceptual reference architecture to support proof-of-concept implementations as well as to support interoperability in distributed infrastructures. Conclusions and an outlook on future work complete the paper.

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.

Genomics-based diagnostic data (GBDD) are becoming increasingly important for laboratory diagnostics. Due to the large quantity of data and their heterogeneity, GBDD poses a big data challenge. Current analysis tools for GBDD are primarily designed for research and do not meet the requirements of laboratory diagnostics for automation, reliability, transparency, reproducibility, robustness, and accessibility. This makes it difficult for laboratories to use these tools in tests that need to be validated according to regulatory frameworks and to execute tests in a time- and cost-efficient manner. In order to better address these requirements, we propose an event-driven workflow-based architecture as the basis for a processing platform that is highly scalable using container technologies and microservices. A prototype implementation of this approach, called GenomicInsights, has been developed and evaluated to demonstrate its feasibility and suitability for laboratory diagnostics.

Manual labeling and categorization are extremely time-consuming and, thus, costly. AI and ML-supported information systems can bridge this gap and support labor-intensive digital activities. Since it requires categorization, coding-based analysis, such as qualitative content analysis, reaches its limits with large amounts of data and could benefit from AI and ML-based support. Empirical social research, its application domain, benefits from Big Data’s ability to create more extensive human behavior and development models. A range of applications are available for statistical analysis to serve this purpose. This paper aims to implement an information system that supports researchers in empirical social research in performing AI-supported qualitative content analysis. AI2VIS4BigData is a reference model that standardizes use cases and artifacts for Big Data information systems that integrate AI and ML for user empowerment. Thus, this work’s concepts and implementations try to achieve an AI2VIS4BigData-compliant information system that supports social researchers in categorizing text data and creating insightful dashboards. Thereby, the text categorization is based on an existing ML component. Furthermore, it presents two evaluations that were conducted for these concepts and implementations: a qualitative cognitive walkthrough assessing the system’s usability and a quantitative user study with 18 participants revealed that though the users perceive AI support as more efficient, they need more time to reflect on the recommendations. The research revealed that AI support increased the correctness of the users’ categorizations but also slowed down their decision-making. The assumption that this is due to the UI design and additional information for processing requires follow-up research.