The purpose of this article is to illustrate an investigation of methods that can be effectively used to predict the data incompleteness of a dataset. Here, the investigators have conceptualized data incompleteness as a random variable, with the overall goal behind experimentation providing a 360-degree view of this concept conceptualizing incompleteness of a dataset both as a continuous, discrete random variable depending on the aspect of the required analysis. During the course of the experiments, the investigators have identified Kolomogorov–Smirnov goodness of fit, Mielke distribution, and beta distributions as key methods to analyze the incompleteness of a dataset for the datasets used for experimentation. A comparison of these methods with a mixture density network was also performed. Overall, the investigators have provided key insights into the use of methods and algorithms that can be used to predict data incompleteness and have provided a pathway for further explorations and prediction of data incompleteness.

Let $\mathsf{T}[1,n]$ be a string of length n and $\mathsf{T}[i,j]$ be the substring of $\mathsf{T}$ starting at position i and ending at position j. A substring $\mathsf{T}[i,j]$ of $\mathsf{T}$ is a repeat if it occurs more than once in $\mathsf{T}$; otherwise, it is a unique substring of $\mathsf{T}$. Repeats and unique substrings are of great interest in computational biology and information retrieval. Given string $\mathsf{T}$ as input, the Shortest Unique Substring problem is to find a shortest substring of $\mathsf{T}$ that does not occur elsewhere in $\mathsf{T}$. In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over $\mathsf{T}$ answering the following type of online queries efficiently. Given a range $[\alpha ,\beta ]$, return a shortest substring $\mathsf{T}[i,j]$ of $\mathsf{T}$ with exactly one occurrence in $[\alpha ,\beta ]$. We present an $\mathcal{O}(nlogn)$-word data structure with $\mathcal{O}\left({log}_{w}n\right)$ query time, where $w=\Omega (logn)$ is the word size. Our construction is based on a non-trivial reduction allowing for us to apply a recently introduced optimal geometric data structure [Chan et al., ICALP 2018]. Additionally, we present an $\mathcal{O}\left(n\right)$-word data structure with $\mathcal{O}\left(\sqrt{n}{log}^{ϵ}n\right)$ query time, where $ϵ>0$ is an arbitrarily small constant. The latter data structure relies heavily on another geometric data structure [Nekrich and Navarro, SWAT 2012].

Machine learning operations, or MLOps, are a set of practices to deploy and maintain machine learning models in production reliably and efficiently. This paper evaluates and compares open-source MLOps tools as a whole through GitHub data. Unsupervised machine learning models are implemented to find topics from repository descriptions and uses these topics to create clusters of repositories. From this analysis, the authors found the MLOps space is dominated by educational and cloud-platform-related content and is growing quickly, especially in the Python language. This paper aims to understand better the available open-source tools at a high level, looking at the types of tools that exist and how they might impact the adoption of MLOps.

Leveraging Artificial Intelligence (AI) ethically in connected healthcare systems requires a quantifiable framework that measures not only outcome correctness, but also the clarity, auditability, and ethical acceptability of model explanations in high-stakes clinical and cybersecurity workflows. This manuscript first presents a narrative review of ethical risks and countermeasures in Healthcare Internet of Things (HIoT) and explains why existing performance metrics are insufficient for trustworthy deployment. We then formalize a quantitative metric called Ethical Explainability (E_e) as a composite index integrating (1) a Human Agreement Ratio (HAR), capturing concordance between AI recommendations (and their rationale) and a calibrated expert consensus, and (2) an Entropy Reduction Index (ERI), capturing the proportional reduction in expert uncertainty after receiving an explanation, operationalized via probability-elicitation questionnaires mapped to Shannon entropy. Designed for HIoT security monitoring, E_e links transparency with governance-ready evidence of trustworthiness for human–AI collaboration.

With the rapid advancements in artificial intelligence (AI) and machine learning (ML), the role of data engineering has become increasingly critical due to the growing demands for high-quality, large-scale, and well-structured datasets required to train reliable predictive models. Healthcare is one of the most data-intensive industries and has demonstrated strong potential for AI-driven automation in clinical decision support, diagnostics, and operational efficiency. However, healthcare data is often fragmented across multiple systems, inconsistently formatted, and constrained by privacy and regulatory requirements, creating significant barriers to scalable AI adoption. In this review, we examine recent research on healthcare data engineering and AI applications, focusing on how data pipelines, interoperability, and governance frameworks support or limit real-world deployment. This review examined 68 peer-reviewed studies published between 2018 and 2026 across multiple clinical domains, including oncology, cardiovascular disease, infectious disease, neurological disorders, medical imaging, and algorithmic frameworks for explainability and fairness. The reviewed literature shows that while AI models achieve promising performance across these domains, the lack of standardized data architectures and interoperable infrastructure remains a primary bottleneck. The purpose of this study is to highlight key challenges and emerging solutions in healthcare data engineering and outline the future directions needed to support safe, scalable, and trustworthy AI integration in the United States healthcare system. The intended core contributions of this article are to: (i) identify the need for reliable AI systems for healthcare, (ii) explore challenges associated with implementing AI systems in healthcare from a data engineer’s perspective, and (iii) analyze key limitations of data engineering as it applies to the implementation of AI systems in healthcare. It must be noted that one of the key limitations of this narrative review is that the authors mostly used citations from MDPI journals.