Academic literature on the topic 'Real-timeData'

Maritime security and surveillance have become one of the main areas in managing overall situational awareness.For example, the growing importance of maritime traffic in cross-border trade has created new pressures to develop newtechnologies for accident prevention, especially in the ports. Maritime safety is also a matter of concern for continuitymanagement. Automatic ship alarm systems, coastal radars and coastal cameras are not alone sufficient equipment to buildmaritime awareness. The Universal Shipborne Automatic Identification System (AIS) is a ship transponder system that is aglobally used tracking system, but highly vulnerable to hacking. A major maritime traffic problem arises if transponders areswitched off. Hybrid threats need coordinated hybrid responses; therefore, a cyber situational picture is also needed. Cybersituational awareness is an essential part of the management of maritime situational awareness. The lack of using real-timedata from the maritime actors affects the correct formation of the common situational picture—for example, from the siteof an accident. Cyber security is an essential factor in developing fairway navigation and all terminal (port-to-port) activities.This research will be done as a part of the SMARTER (Smart Terminals) -project that belongs to the SEA4VALUE program. Theproject aims to develop unique digitalized concepts that enhance safe transportation and reduce emissions in the port andthe terminal areas. By using the multiagent system with sensor technology, e.g., in the harbors, it is possible to gather andshare meaningful maritime security-related data. The study's primary purpose is to describe the operating environment andmake an initial analysis of system requirements for optimizing situational awareness in the area of western ports of Finland

This paper explores the transformative role of real-time data and cloud data warehousing in optimizing supply chain analytics. As supply chains grow increasingly complex, businesses are increasingly turning to data-driven strategies to enhance operational efficiency, reduce costs, and improve responsiveness to market demands. Real-time data provides the foundation for proactive decision-making, enabling organizations to monitor supply chain activities continuously and make informed adjustments to minimize disruptions. The integration of cloud data warehousing offers scalable, flexible, and cost-efficient solutions for managing vast quantities of supply chain data, allowing for seamless data centralization, integration, and analytics. This strategic framework, combining real-time data with cloud platforms, not only improves operational agility but also enhances the ability to predict demand, optimize inventory, and mitigate risks. Despite the substantial benefits, challenges related to data integration, organizational resistance, and security concerns persist. This paper outlines these challenges and proposes strategies for overcoming them. Furthermore, it highlights future research directions, including the application of artificial intelligence, the role of IoT in data collection, and the ethical and regulatory considerations of cloud adoption. Ultimately, the integration of these technologies is poised to reshape the future of supply chain analytics, fostering greater efficiency and competitiveness in an increasingly dynamic global marketplace.

The integration of Full Electric Vehicles (FEVs) into the smart city ecosystemis an essential step towards achieving sustainable urban mobility. This study presentsa comprehensive mobility network model designed to predict and optimize the energysupply for FEVs within smart cities. The model integrates advanced components such asa Charge Station Control Center (CSCC), smart charging infrastructure, and a dynamicuser interface. Important aspects include analyzing power consumption, forecasting urbanenergy demand, and monitoring the State of Charge (SoC) of FEV batteries usinginnovative algorithms validated through real-world applications in Valencia (Spain) andLjubljana (Slovenia). Results indicate high accuracies in SoC tracking (error < 0.05%) andenergy demand forecasting (MSE ~6 × 10−4), demonstrating the model’s reliability andadaptability across diverse urban environments. This research contributes to the developmentof resilient, efficient, and sustainable smart city frameworks, emphasizing real-timedata-driven decision-making in energy and mobility management.

Health insurance industry’s digital transformation involves the adoption of two important revolutionarytechnologies namely Artificial Intelligence (AI) and cloud computing to transform the traditional insuranceprocesses. AI helps insurers to process large volumes of data for rationalisation of activities including claims,frauds, and customer services. Using an advanced machine learning technique, patterns can be identified as wellas outcomes predicted and various policies can be sold based on a confirmed individual customer need (Smith etal., 2020). On the same note, cloud computing offers insurers and policyholders’ secure, available, and real-timedata storage and management solutions that are also elastic. The integration of AI and cloud has improvedoperational efficiency, optimised costs and increased client happiness while creating the building blocks for asmarter, more adaptable and revolutionised health insurance market. This has made it possible for the insurers tosolve numerous hard issues utilising AI and cloud solutions as well as offering services that meet the newgeneration customers’ expectations in the new digital economy world.AI combined with cloud solutions can effectively solve a number of the long-standing problems relevant to thefunctioning of health insurance businesses, primarily in terms of risk assessment, compliance with regulatoryrequirements, and customer service. By using advanced data analysis, AI helps insurers identify riskcharacteristics that have never been previously imaginable, Pand.it will improve the insurance policy pricingcompetitiveness and fairness (Johnson & Lee, 2020). For instance, a healthcare insurer may use computerenhanced models to evaluate the specific health data, life styles and demographic factors of a certain person andcome up with anticipated future healthcare costs with a view of designing relevant products. On the hand, cloudplatforms have a significant role of bridging insurers, healthcare providers and customers through enhancedsystems that increase openness and real-time interaction. Such systems guarantee compliance with highlypressuring regulation measures, adopting techniques such as encryption of health information, authenticationtechniques, and secure storage (Williams et al., 2020). Furthermore, the combination of the AI approach and cloudsolutions has had a greater effect in new markets when more traditional models of health insurance do not work.Such technologies, therefore, allow insurers to penetrate such regions profitably and serve hitherto unserved orunder-served markets with timely and affordable solutions. The future of the health insurance industry deeperintegration of two of the most important technologies – AI and cloud will help the industry transform, put thefocus on prevention, wellness, and customer experiences. A major potential is the implementation of successfulAI optimised ecosystems that interface with cloud solutions to predict the overall health trends, conduct earlydiagnostic searches and even proposing heath promotion solutions. For instance, wearable health devicesconnected with cloud platforms can receive actual patient data, allow the insurers of transforming the policy’sterms or offering the bonuses for the activity levels or vital signs. Further, the adoption of Natural LanguageProcessing (NLP) has improved customers/ policyholder interaction based on intelligent/natural language andthrough chatbots and virtual assistants and ai-based call centers, thus providing an easy to interact and operateinterface (Miller et al., 2020). Such a solution not only enhances the overall level of customers’ satisfaction butalso enhances the insurer’s business processes, decreasing the administrative burden. In the same manner, asapplied technologies in artificial intelligence expand continual upgrades are made to fraud detection in insurancemaking it easier for the insurers to detect the anomalies and prevent fraudulent claims.AI and Cloud computing is transforming the health insurance industry at an exponential rate and when combinedaffects more than the efficiency and reliability metrics. They play an important role in building fair and sustainablegeneration of insurance solutions for various clients and appropriate for the world conditions. As such, thesetechnologies should create a new era of co-creation, personalized medicine, and data-driven health insurance tobenefit all organizational stakeholders and set a high-level benchmark within the digital health insuranceecosystem.