Real-time monitoring of streaming data is crucial, especially when dealing with unstructured text data, which is increasingly prevalent in our daily activities. This type of data requires special attention in various industries. For instance, in the travel and hospitality sector, businesses monitor customer reviews, travel forums, and social media to assess service quality, satisfaction, and emerging trends. In public safety, national security, and biosurveillance, monitoring online forums, social media, and news outlets is essential for identifying threats, criminal activity, or public safety concerns. Additionally, the challenge of efficiently monitoring unstructured text streams, such as business emails, is a significant issue for large organizations. In this paper, we propose a method that combines natural language processing and text visualization techniques with traditional process monitoring algorithms to enhance the analysis and understanding of text streams. Our method involves mapping text streams onto a bivariate plot, followed by the application of monitoring techniques on a sequence of statistics derived from these plots. This sequential analysis reveals valuable insights into temporal patterns and fluctuations. We integrate, in the proposed method, two robust real-time monitoring procedures—Cumulative Sum and Pruned Exact Linear Time—both widely used for change point detection, allowing the framework to support also retrospective analysis for extracting critical insights. The effectiveness of this framework is demonstrated through simulations and two real-world case studies. The first case study involves the BBC, where the framework is used to detect extreme events through published articles. The second, provided by a shipbroker in Greece, applies the framework to monitor a large volume of emails. Ultimately, the proposed solution provides a comprehensive approach to dynamically monitoring time-varying unstructured text streams, offering organizations a powerful tool for informed decision-making and improved market intelligence.

The maritime is facing a gradual proliferation of data, which is frequently coupled with the presence of subpar information that contains missing and duplicate data, erroneous records, and flawed entries as a result of human intervention or a lack of access to sensitive and important collaborative information. Data limitations and restrictions have a crucial impact on inefficient data-driven decisions, leading to decreased productivity, augmented operating expenses, and the consequent substantial decline in a competitive edge. The missing or inadequate presentation of significant information, such as the vessel’s primary engine model, critically affects its capabilities and operating expenses as well as its environmental impact. In this study, a comprehensive study was employed, using and comparing several machine learning classification techniques to classify a ship’s main engine model, along with different imputation methods for handling the missing values and dimensionality reduction methods. The classification is based on the technical and operational characteristics of the vessel, including the physical dimensions, various capacities, speeds and consumption. Briefly, three dimensionality reduction methods (Principal Component Analysis, Uniform Manifold Approximation and Projection, and t-Distributed Stochastic Neighbor Embedding) were considered and combined with a variety of classifiers and the appropriate parameters of the dimensionality reduction methods. According to the classification results, the ExtraTreeClassifier with PCA with 4 components, the ExtraTreeClassifier with t-SNE with perplexity equal to 10 and 3 components, and the same classifier with UMAP with 10 neighbors and 3 components outperformed the rest of the combinations. This classification could provide significant information for shipowners to enhance the vessel’s operation by optimizing it.