The problem of designing and developing information system applications is like sitting on the fence because the knowledge of two very different domains must be aligned, i.e. the business domain the application refers to and the information system engineering domain responsible for the application’s design and development. In this article ‘activity-based cost GHG accounting’ is selected due to its recent appearance for solving the design and development problem by establishing a ‘real-time add-on’ digital GHG accounting information system (GHG-AIS) application with the ‘object-oriented’ domain engineering methodology. The ‘real-time’ capability is the key feature of the digital AIS application that distinguishes it from a ‘manual’ – e.g. spreadsheet – application, and the ‘add-on’ feature assures compatibility to the production information systems incorporated in existing ERP systems.
Machine learning models are increasingly used to explore survival and treatment outcomes for stroke patients. This study addresses that gap by applying causal inference methods to estimate the survival impact of different stroke treatment strategies by using real-world clinical data. The investigation was conducted by analysing records from 944 stroke patients treated at the Clinical Centre of Montenegro. The data variables included demographic characteristics, clinical status, stroke type, and treatment information. The proposed method addressed the observed problem of high deviation and differences in sensitivity across medical treatment types within patient groups with the same stroke type diagnosis. Propensity Score Matching was used to construct comparable patient groups based on demographic and medical data and to reduce bias inherent in observational data. The causal forests machine learning method was applied to explore causal relationships and estimate individual patterns that affect survival and recovery across patient subgroups. The applied method revealed age, gender, and health status effects, highlighting differences in patient responses to treatment, as compared to the comparative evaluation without group matching and causal effect research.
Business process execution can deviate from expected behavior, resulting in anomalous events or cases. Detecting such anomalies is important for ensuring compliance, reducing risks, and improving process reliability. Existing research has mainly addressed this problem in offline settings using unsupervised learning approaches. However, such approaches detect anomalies retrospectively on complete logs and do not leverage the anomaly labels provided by domain experts. In this paper, we propose STAMP, an approach for Semi-supervised sTreaming AnoMaly detection using next activity Prediction. STAMP integrates a next-activity prediction model with an anomaly classification model trained on a limited number of anomaly labels from domain experts. Both models are continuously updated in a streaming setting to capture recent process executions. We evaluate STAMP on benchmark event logs generated with three different noise levels. Our findings demonstrate that STAMP can improve recall in early anomaly detection compared with a fixed-threshold baseline while requiring only a modest number of labeled anomalies. These findings show how semi-supervised learning can leverage scarce expert feedback for anomaly detection in streaming process monitoring.
Honey robbing in honey bees is a fast, destructive event between colonies that can rapidly deplete food reserves, cause colony loss, and facilitate the spread of parasites and pathogens within an apiary. Early detection remains challenging because visual signs at early stages are often indistinguishable from normal foraging activity. This paper proposes a data-driven early warning approach for honey robbing based on online IoT monitoring of smart hives, with a focus on data quality, reproducibility, and readiness for integration into beehive monitoring systems. The study is based on a homogeneous network of ten AmoHive smart hives observed during a single nectar season, including one confirmed robbing case and nine reference hives. The paper contributes: (i) an open dataset combining raw IoT logs with standardized, cleaned, and quality-controlled time series together with a documented preprocessing workflow and provenance information; (ii) a formalization of a hidden early warning window from 10 to 1 days before the overt event, during which destructive weight loss accumulates without obvious external symptoms; and (iii) a lightweight and interpretable cumulative detector based on standard deviation, inter-hive normalization, and cumulative negative deviation, conceptually linked to approaches from statistical process control and negative cumulative summation. In the documented case, the system generated stable warnings several days before the overt robbing phase without false alarms in the studied network, demonstrating practical feasibility for precision beekeeping.
Public sector organizations increasingly procure AI-enabled ICT systems to support decision-making and service delivery. Although ethical AI frameworks emphasize transparency, accountability, and human oversight, these principles are rarely translated into explicit requirements in procurement processes. Consequently, human-AI interaction (HAI) is often left to vendor design choices. This paper conceptualizes HAI as a procurement-critical design dimension and proposes a taxonomy of interaction requirements tailored to public sector ICT procurement. The taxonomy enables contracting authorities to specify and govern interaction properties through procurement instruments, supporting both ethical compliance and sustainable value realization.
