High utility pattern mining (HUPM) is one of the key areas in data mining, which is concerned with identifying patterns with high utility from transactional databases. The temporal factors such as periodicity and recency along with dynamic variations in profits have recently been added to pattern mining. However, no methods so far unify these dimensions in a common framework. To this end, in this paper we propose the DTU-Miner algorithm that integrates temporal constraints and dynamic profit updates to overcome such limitations. Through the use of advanced data structures such as UPR-List and P-set and the introduction of some novel pruning strategies, DTU-Miner surpasses state of the art in terms of Runtime, Memory and pattern quality. Results on benchmark datasets show that DTU-Miner outperforms state-of-the-art algorithms, CPR-Miner and iEFIM-Closed, which suggests the effectiveness of DTU-Miner over dense and sparse datasets including dynamic attributes.
Harsh industrial environments such as oilfields present unique challenges to electronic systems, including extreme temperatures, limited connectivity, power constraints, and operational unpredictability. Traditional Internet of Things (IoT) deployments often fail to adapt in real-time, exposing systems to risks such as data loss, late anomaly detection, or critical failure. This paper proposes a lightweight, Artificial Intelligence (AI)-driven eSystem architecture tailored for such conditions, integrating edge intelligence, secure communication, and self-adaptive mechanisms. We demonstrate the framework's viability through simulating a case study of real-time sensor data from pipeline infrastructure, applying a Long Short-Term Memory (LSTM)-based anomaly detection model deployed at the edge. Results show significant improvements in detection latency, bandwidth efficiency, and system resilience. The framework offers a modular blueprint for deploying AI-enhanced eSystems across energy, mining, and remote critical infrastructure domains.