Journal of Data Science and Information Technology

Evaluating Development Strategies for Connected App Using Copras Methodology

Mr. Vedaswaroop Meduri — 2025-01-22

The BMW Connected app is a digital companion designed to enhance the driving experience by seamlessly integrating your smartphone with your BMW. It offers a range of intelligent features, including remote control of your vehicle, real-time vehicle status updates, and navigation assistance. With functionalities like remote locking/unlocking, locating your car, and scheduling trips based on traffic conditions, the app ensures convenience and efficiency. It also integrates with smart home devices and personal calendars for a connected lifestyle. The BMW Connected app prioritizes user experience, delivering personalized services that make driving smarter, safer, and more enjoyable.

The BMW Connected app is a pivotal innovation in the automotive industry, offering seamless integration between drivers and their vehicles. Its research significance lies in its ability to enhance user experience through personalized mobility services, remote vehicle control, and real-time navigation updates. It exemplifies the shift towards smart, connected cars by leveraging IoT, AI, and cloud-based technologies. Exploring this app provides insights into user behavior, digital transformation in automotives, and the potential for improving safety, convenience, and sustainability. Additionally, it highlights the importance of data security and privacy in connected systems, making it a rich subject for technological and consumer-focused research.

The BMW Connected app methodology focuses on seamless integration of digital services to enhance the driving experience. It utilizes cloud-based intelligence and the Internet of Things (IoT) to connect the driver, vehicle, and external devices. The app leverages user data and preferences to provide personalized services like navigation, remote vehicle control, and real-time traffic updates. It incorporates features such as remote lock/unlock, vehicle status monitoring, and preconditioning. Machine learning algorithms adapt to user behavior, offering predictive recommendations and route planning. Secure data encryption ensures user privacy. The app’s ecosystem supports continuous updates, ensuring compatibility with evolving technology and user needs.

Continue iOS-focused Dev, Accelerate Android Dev, Develop Cross-platform App, Outsource Android Dev, Focus on Advanced AI Features

User Engagement, Market Share Growth, Revenue Generation, Brand Perception

Accelerate Android Dev is getting first place of the table and Focus on Advanced AI Features is getting last place of the table

Evaluating IoT Platforms: An Approach Using the COPRAS Method

Satya Ballamudi — 2025-01-18

IoT platforms act as technological frameworks that provide the foundation for connecting and managing Internet of Things devices and applications. These platforms offer a wide range of services and tools that streamline the development, deployment, and operation of IoT solutions. They enable seamless integration and communication between IoT devices, facilitate data collection and analysis, provide device management capabilities, and facilitate the creation of IoT applications. By offering a centralized and scalable infrastructure, IoT platforms play a crucial role in empowering organizations and developers to fully harness the potential of the IoT, leading to the creation of innovative and efficient IoT solutions. Research dedicated to “the selection of IoT platforms plays a crucial role in the industry”. “With the increasing number of IoT applications”, the importance of making the right platform choice becomes critical for successful implementation.

The research provides valuable insights that aid organizations and developers “in making informed decisions when selecting an IoT platform that aligns with their specific requirements”. By leveraging this knowledge, stakeholders can ensure that they choose the most suitable platform to meet their needs effectively. “The objective of this research paper is to tackle the evaluation of IoT platforms” by approaching it as a problem of multicriteria decision making (MCDM) due to its complexity involving multiple factors. To accomplish this goal, the research develops a system for creating evaluation criteria, facilitating the comprehensive assessment of IoT platforms. In the ranking based on the COPRAS method, Google Cloud IoT emerged as the top-ranked platform, demonstrating its superior performance and highest utility. Amazon AWS IoT Core closely followed in the second position, showcasing its strong performance and positive attributes.

Microsoft Azure IoT Hub secured the third rank, highlighting its competitive performance compared to other platforms. ThingWorx obtained the fourth rank, indicating its relatively good performance according to the COPRAS method. Particle ranked fifth, positioning its performance in the middle range among the evaluated platforms. Oracle IoT obtained the sixth rank, suggesting its performance was relatively lower compared to other platforms. IBM Watson IoT received the seventh rank, indicating its relatively weaker performance in the evaluation. These rankings offer valuable insights for decision-making and platform selection, enabling stakeholders to evaluate the overall performance and relative positions of the IoT platforms based on the COPRAS method.

Implementing And Evaluating Sap Solutions at The Union Pacific Railroad Multi-Criteria Decision Analysis Using the COPRAS Method

Nitesh Kumar Ramancha — 2025-02-21

Introduction:Union Pacific is making a transformational effort by adopting SAP solutions to improve its operations. The company is migrating its back-office systems to SAP S/4HANA Cloud with the aim of streamlining finance, sourcing, logistics, human resources and cost management. The transition will help reduce technical debt, simplify processes and improve decision-making through better data integration. In addition, Union Pacific is using SAP Transportation Management (TM) to improve logistics, focusing on improving freight movement, carrier management, bidding, tendering and invoicing, which is an operational

Research significance:The integration of SAP Transportation Management (TM) and SAP S/4HANA cloud migration within Union Pacific delivers significant improvements in logistics optimization, operational efficiency, and decision-making. By using SAP TM, Union Pacific is improving freight movement, carrier management, bidding, tendering, and invoicing, directly improving the speed and accuracy of service delivery. This technology transformation allows suppliers to better manage supplier data and conduct smooth business transactions by providing training materials and support tools to help them register and manage their profiles within the SAP business network. Seamless data exchange ensures that supplier information is accurate and up-to-date, further facilitating efficient business operations.

Methology: The alternative options for infrastructure are SAP S/4HANA Cloud, SAP Ariba, SAP TransportationManagement, Mobile Plant Maintenance App, Supplier Registration and Training, and System Integration, Data Analytics and Reporting. The evaluation criteria Operational Efficiency, Data Integration and Accuracy,Implementation Costs, Disruption During Transition.

Result: According to the results, Data Analytics and Reporting was ranked highest, while Mobile Plant Maintenance App was ranked lowest.

Conclusion:Data Analytics and Reporting has the highest value for Union Pacific Railroad Company according to the COPRAS methods approach.

Keywords: Union Pacific,SAP TM, Freightmovement, Logistics, Carriermanagement, Bidding, Tendering,Invoicing, Operational efficiency, Service delivery.

Edge-Cloud Continuum for Ai-Driven Remote Patient Monitoring: A Scalable Framework

Santhosh Kumar Pendyala — 2025-01-15

The rapid advancements in healthcare technology, coupled with the increasing demand for real-time patient monitoring, have catalyzed the development of innovative frameworks such as Edge-Cloud convergence. This study presents a scalable architecture leveraging the edge-cloud continuum to enhance remote patient monitoring systems. By integrating lightweight edge devices for real-time anomaly detection and cloud platforms for comprehensive data analytics, the framework addresses critical challenges in latency, scalability, and computational efficiency.Statistical evidence underscores its efficacy: latency reductions of up to 40% and a 20% improvement in early anomaly detection accuracy have been observed in hospital trials involving ICU patients. The framework incorporates IoT devices such as ECG monitors and pulse oximeters, edge gateways for data preprocessing, and AI models retrained in the cloud for continuous optimization.Tools like MQTT, Kafka, and TensorFlow Lite ensure seamless data transmission and efficient AI model deployment, while Apache Spark enhances batch data processing capabilities. By bridging the gap between local computation and centralized data processing, the framework offers a robust solution to modern healthcare challenges, particularly in pandemic scenarios requiring rapid scaling.This study demonstrates how the convergence of AI, edge computing, and cloud platforms can transform patient monitoring systems, delivering scalable, real-time healthcare solutions.

Cloud Computing Environment Optimization: A Comprehensive COPRAS Methodology Approach

Janaki Ram Kiran Gummaluri — 2025-01-13

Cloud computing has emerged as a transformative technology, offering diverse service models to meet varying organizational needs. This study employs the Complex Proportional Assessment (COPRAS) method to comprehensively evaluate and analyze different cloud environments, providing a nuanced approach to cloud service selection. The research investigates five distinct cloud environment types: general-purpose, service-centric, zone-centric, distance-centric, and cost-centric, examining their performance across critical parameters including quality of service, number of available services, availability zones, consumer distance, and hourly cost.

The multi-criteria analysis reveals significant variations in cloud environment effectiveness. Zone-centric environments emerged as the top performer, achieving a remarkable quality index of 0.291 and a 100% usability degree. Service-centric environments followed closely, demonstrating a 92.03% usability degree and highlighting the importance of service availability. General-purpose environments showed moderate performance with a 63.97% usability degree, indicating their versatility. Conversely, distance-centric and cost-centric environments exhibited the lowest performance, suggesting limitations in meeting comprehensive organizational requirements.

Key findings underscore that cloud computing is not a one-size-fits-all solution but a complex ecosystem requiring strategic selection. The research emphasizes that while cost is important, it should not be the sole determining factor in cloud environment selection. The study provides a robust framework for decision-makers, enabling them to align cloud infrastructure choices with specific organizational objectives. The methodology offers critical insights into the evolving cloud computing landscape, addressing the growing complexity of data management and the increasing demand for scalable, secure computing solutions. By presenting a comprehensive evaluation approach, the research contributes to a more sophisticated understanding of cloud environment selection, encouraging organizations to adopt a strategic, multi-dimensional approach to cloud infrastructure deployment.