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Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.

Decision support systems (DSS) integrating artificial intelligence (AI) hold the potential to significantly enhance organizational decision-making performance and speed in areas such as prognostics in machine maintenance. A key issue for organizations aiming to leverage this potential is to select an appropriate AI-based DSS. In this paper, we develop a delegation perspective to identify decision factors and underlying AI system characteristics that affect the selection of AI-based DSS. Utilizing the analytical hierarchy process method, we derive decision weights for these characteristics and apply them to three archetypes of AI-based DSS designed for prognostics. Additionally, we explore how users’ expertise levels impact their preferences for specific AI system characteristics. The results confirm that Performance is the most important decision factor, followed by Effort and Transparency. In line with these results, we find that the archetypes of prognostics systems using Direct Remaining Useful Life estimation and Similarity-based Matching best fit user preferences. Moreover, we find that novices and experts strongly prefer visual over structural explanations, while users with moderate expertise also value structural explanations to develop their skills further.

In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step. However, these experts are not always available and data mining novices may be required to perform the task. While there are several research efforts for automated method selection as a means of support, only a few approaches consider the particularities of problems expressed in the natural and domain-specific language of the novice. The study proposes the design of an intelligent assistance system that takes problem descriptions articulated in natural language as an input and offers advice regarding the most suitable class of data mining methods. Following a design science research approach, the paper (i) outlines the problem setting with an exemplary scenario from industrial practice, (ii) derives design requirements, (iii) develops design principles and proposes design features, (iv) develops and implements the IT artifact using several methods such as embeddings, keyword extractions, topic models, and text classifiers, (v) demonstrates and evaluates the implemented prototype based on different classification pipelines, and (vi) discusses the results’ practical and theoretical contributions. The best performing classification pipelines show high accuracies when applied to validation data and are capable of creating a suitable mapping that exceeds the performance of joint novice assessments and simpler means of text mining. The research provides a promising foundation for further enhancements, either as a stand-alone intelligent assistance system or as an add-on to already existing data science and analytics platforms.

Taxonomies can serve as a valuable tool to capture dimensions and characteristics of data analytics solutions in a structured manner and thus create transparency about different design options of the technical solution space. However, previous taxonomic approaches often remain at a purely descriptive level without leveraging morphological structures to investigate the mechanisms between different combinatorial options given in data analytics pipelines. To this end, we propose a taxonomic evaluation approach to evaluate and construct the technical core of analytical information systems more systematically. Specifically, we present a rough guidance model consisting of four steps, which we subsequently instantiate with two application scenarios from the fields of industrial maintenance and predictive business process monitoring. In this way, we demonstrate how taxonomic frameworks can guide the creation of structured evaluation studies to consider the construction and assessment of data analytics pipelines in a multi-perspective and holistic manner. Our approach is sufficiently generic to be applied to various domains, scenarios, and decision support tasks.

Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection. In this paper, we provide a comprehensive survey of recent developments from 2012–2021 in 3D object detection covering the full pipeline from input data, over data representation and feature extraction to the actual detection modules. We introduce fundamental concepts, focus on a broad range of different approaches that have emerged over the past decade, and propose a systematization that provides a practical framework for comparing these approaches with the goal of guiding future development, evaluation, and application activities. Specifically, our survey and systematization of 3D object detection models and methods can help researchers and practitioners to get a quick overview of the field by decomposing 3DOD solutions into more manageable pieces.

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient’s complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance – where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees – and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.

The booming adoption of Artificial Intelligence (AI) likewise poses benefits and challenges. In this paper, we particularly focus on the bright side of AI and its promising potential to face our society’s grand challenges. Given this potential, different studies have already conducted valuable work by conceptualizing specific facets of AI and sustainability, including reviews on AI and Information Systems (IS) research or AI and business values. Nonetheless, there is still little holistic knowledge at the intersection of IS, AI, and sustainability. This is problematic because the IS discipline, with its socio-technical nature, has the ability to integrate perspectives beyond the currently dominant technological one as well as can advance both theory and the development of purposeful artifacts. To bridge this gap, we disclose how IS research currently makes use of AI to boost sustainable development. Based on a systematically collected corpus of 95 articles, we examine sustainability goals, data inputs, technologies and algorithms, and evaluation approaches that define the current state of the art within the IS discipline. This comprehensive overview enables us to make more informed investments (e.g., policy and practice) as well as to discuss blind spots and possible directions for future research.

Condition-based maintenance (CBM) has emerged as a proactive strategy for determining the best time for maintenance activities. In this paper, a case of a milling process with imperfect maintenance at a German automotive manufacturer is considered. Its major challenge is that only data with missing labels are available, which does not provide a sufficient basis for classical prognostic maintenance models. To overcome this shortcoming, a data science study is carried out that combines several analytical methods, especially from the field of machine learning (ML). These include time-domain and time–frequency domain techniques for feature extraction, agglomerative hierarchical clustering and time series clustering for unsupervised pattern detection, as well as a recurrent neural network for prognostic model training. With the approach developed, it is possible to replace decisions that were made based on subjective criteria with data-driven decisions to increase the tool life of the milling machines. The solution can be employed beyond the presented case to similar maintenance scenarios as the basis for decision support and prognostic model development. Moreover, it helps to further close the gap between ML research and the practical implementation of CBM.