Real-Time Data Processing for Inventory Optimization in Distributed Cloud Systems

In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.

Recent advances in BazookaSPECT: Real-time data processing and the development of a gamma-ray microscope

Real-time big data processing is a crucial need of meteorology today. As a matter of fact, the weather is a determining factor in decision making in different areas such as air or sea transport, so acquiring knowledge of the environment in real time is a consequential question. A promising approach for big data treatment in real time is based on the concept of complex events processing CEP). The CEP aims to identify significant events in a cloud of simple and individually detectable events in real time. The meteorological variables, constituting simple event's spaces, offer significant flow of data to be processed using CEP technologies. This real-time data processing is done by phase of their integration with weather systems. This paper discusses the big data's meteorological aspects, and shows the architectural model for integration of weather data and its processing in real time.

This comprehensive article explores the transformative impact of cloud computing technologies on the retail industry, examining core platform solutions, implementation strategies, and future trends. The article investigates how cloud integration has revolutionized inventory management, demand forecasting, and operational efficiency across the retail sector. Through detailed case studies of major retailers, the research demonstrates the substantial benefits of cloud adoption in areas such as real-time data processing, predictive analytics, and supply chain optimization. The article encompasses technical considerations, best practices, and emerging technologies, providing insights into successful implementation strategies and operational guidelines. The article further evaluates the integration opportunities and standardization initiatives shaping the future of retail cloud computing, highlighting the critical role of artificial intelligence, blockchain, IoT, and advanced analytics in driving industry innovation.

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

Real-time big data processing for anomaly detection: A Survey

Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide these smart services quickly, efficiently, in an interoperable manner and in real time is a cutting-edge technological challenge. Although some software architectures offer solutions in this area, these are often limited in terms of reusability and maintenance by independent modules —involving the need for system downtime when maintaining or evolving, as well as by a lack of standards in terms of the interoperability of their interface. In this paper, we propose a fully reusable microservice architecture, standardized through the use of the Web of things paradigm, and with high efficiency in real-time data processing, supported by complex event processing techniques. To illustrate the proposal, we present a fully reusable implementation of the microservices necessary for the deployment of the architecture in the field of air quality monitoring and alerting in smart ports. The performance evaluation of this architecture shows excellent results.

In a medical emergency situation, real-time patient data sharing may improve the survivability of a patient. In this paper, we explore how Digital Twin (DT) technology can be used for real-time data storage and processing in emergency healthcare. We investigated various enabling technologies, including cloud platforms, data transmission formats, and storage file formats, to develop a feasible DT storage solution for emergency healthcare. Through our analysis, we found Amazon AWS to be the most suitable cloud platform due to its sophisticated real-time data processing and analytical tools. Additionally, we determine that the MQTT protocol is suitable for real-time medical data transmission, and FHIR is the most appropriate medical file storage format for emergency healthcare situations. We propose a cloud-based DT storage solution, in which real-time medical data are transmitted to AWS IoT Core, processed by Kinesis Data Analytics, and stored securely in AWS HealthLake. Despite the feasibility of the proposed solution, challenges such as insufficient access control, lack of encryption, and vendor conformity must be addressed for successful practical implementation. Future work may involve Hyperledger Fabric technology and HTTPS protocol to enhance security, while the maturation of DT technology is expected to resolve vendor conformity issues. By addressing these challenges, our proposed DT storage solution has the potential to improve data accessibility and decision-making in emergency healthcare settings.

Electrical submersible pump (ESP) operation, like many other segments of the oil industry, is recovering from having been drowned in large number of data, the aftermath of information technology revolution, and refocusing on effective utilization of reservoir and production data. This paper presents results of the application of real-time data processing and interpretation in a mature field (Permian basin) through operating and service company alliances. The process involves application of production system efficiency management coupled with newly developed advanced ESP technology and analysis tools-computer simulation software packages that could simulate actual ESP and downhole performance. Data are acquired from the wellsite using data acquisition panels and are sent over a secure network connection to a data hub. The data are also sent for intelligent real-time ESP data processing and interpretation. Well and reservoir monitoring, surveillance, analysis, and diagnosis are also performed. Active collaboration with producing companies at every stage of the process was encouraged through the formation of a new alliance. Finally, various recommendations and suggestions that could operate, optimize the ESP system, reduce well downtime, and prevent catastrophic and premature ESP failures were made. This new approach to ESP operation reduces daily challenges in operating brownfields, such as keeping wells economically viable; solving production problems associated with operating mature assets in today's oil industry that is plagued with continuous declining of manpower resources. As might be expected, lease operating expense (LOE) and/or deferred oil production is reduced. Operational case studies and individual benefits are highlighted and included in this paper. This new approach is a significant departure from the conventional ways of operating ESP where service companies traditionally supply ESPs to producing companies to operate. This new alliance will have a decided influence on future ESP operation and the survival of many brownfields worldwide. Introduction ESP applications as a form of artificial lift method have continued to increase through the years, and significant growth is expected in the next decades as artificial lift requirements increase to a greater extent than current usage. ESP application covers all aspects of hydrocarbon exploitation. Its usage ranges from mature marginal onshore wells to high profile, deep-water subsea wells.

Inventory planning and optimization are crucial components of effective supply chain management, playing a pivotal role in providing a balance between customer demand and operational efficiency. This research article investigates the current landscape of inventory optimization processes, their implementation, and their considerable impact on business performance in the context of recent global supply-chain disruptions and technological advancements. The study also proceeds to examine various characteristics of inventory management, including but not limited to demand forecasting, inventory strategy formulation, and stock replenishment methodologies. By analysis of data from multiple sources and case studies, the effectiveness of different optimization approaches, such as machine learning-based demand forecasting, multi-objective optimization, and advanced inventory models are investigated. The research also delves into the aspects of integration of artificial intelligence and machine learning in developing inventory optimization processes, especially in the face of stochastic demand and supply chain disruptions. Our findings indicate that successful inventory optimization strategies contribute significantly reducing costs, improving customer satisfaction, and increasing overall supply chain resilience. The study reveals that companies implementing data-driven inventory optimization techniques experience a 15-20% reduction in inventory holding costs and a 10-15% improvement in service levels. Additionally, the research highlights the importance technology and human handshake of real-time data analysis, cross-functional collaboration, and adaptive strategies in achieving optimal inventory levels to avoid opportunity cost. The impact of recent global disruptive events, such as the COVID-19 pandemic that was unprecedented, on supply chain management practices is also examined, providing insights into the development of more resilient and flexible inventory systems. This comprehensive examination and evaluation of inventory planning and optimization provides valuable insights for supply chain professionals and researchers, offering a foundation for future advancements in this critical area of business operations in an increasingly volatile and interconnected global market.

Purpose: The study sought to explore the impact of edge computing on real-time data processing. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of edge computing on real-time data processing. Preliminary empirical review reveled that edge computing significantly reduced latency and enhanced efficiency in real-time data processing across various industries by bringing computational resources closer to data sources. It highlighted the technology's ability to handle large volumes of IoT-generated data, improve security by localizing data processing, and drive innovation and economic growth through new applications and services. Edge computing's decentralized approach proved essential for reliable and robust data handling, particularly in critical sectors like healthcare and finance, ultimately solidifying its importance in the digital transformation landscape. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource-Based View (RBV) and Sociotechnical Systems Theory may be used to anchor future studies on edge computing on real-time data processing. The study recommended expanding theoretical frameworks to include the unique aspects of edge computing, investing in robust edge infrastructure, and developing standardized protocols and best practices. It emphasized the need for government incentives and supportive regulatory frameworks to promote adoption, and suggested that academic institutions incorporate edge computing into curricula. Additionally, the study called for ongoing research to address emerging challenges and opportunities, ensuring continuous advancement and effective implementation of edge computing technologies.

In the era of digital transformation, the Internet of Things (IoT) has emerged as a pivotal force in information technology. This research addresses the pressing need for companies to integrate IoT technology, specifically Ultra-High Frequency Radio-Frequency Identification (UHF RFID), into their warehouse systems. The study aims to tackle the challenges faced by companies during peak periods, such as online shopping promotions, where transaction volumes surge, demanding efficient inventory and shipment management. The proposed solution involves implementing an IoT-based warehouse management system leveraging UHF RFID technology. This system seeks to optimize inventory and shipment management through real-time data collection, processing, and analysis. By continuously monitoring inventory and delivery processes, companies can swiftly identify and address issues. The integration of UHF RFID with the information system not only enhances order fulfillment efficiency during peak times but also reduces costs and time associated with inventory management. This research advocates for the strategic adoption of an IoT-based warehouse management system, emphasizing the benefits of UHF RFID integration, including enhanced accuracy, reduced labor costs, and overall improved business efficiency. The findings provide valuable insights into the practical implementation of IoT technologies, offering a roadmap for companies seeking to optimize warehouse operations and meet customer demands with precision in today's competitive digital landscape.

The ability to interpret spatiotemporal data streams in real-time is critical for a range of systems. However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks, and other sources, is a considerable challenge. The major goal of this study is to create a framework for processing and analyzing spatiotemporal data from multiple sources with irregular shapes so that researchers can focus on data analysis instead of worrying about the data sources' structure. We introduced a novel spatiotemporal data paradigm for true-real-time stream processing, which enables high-speed and low-latency real-time data processing, with these considerations in mind. A comparison of two state-of-the-art real-time process architectures was offered, as well as a full review of the various open-source technologies for real-time data stream processing, and their system topologies were also presented. Hence, this study proposed a brand-new framework that integrates Apache Kafka for spatiotemporal data ingestion, Apache flink for true real-time processing of spatiotemporal stream data, as well as machine learning for real-time predictions, and Apache Cassandra at the storage layer for distributed storage in real-time. The proposed framework was compared with others from the literature using the following features: Scalability (Sc), prediction tools (PT), data analytics (DA), multiple event types (MET), data storage (DS), Real-time (Rt), and performance evaluation (PE) stream processing (SP), and our proposed framework provided the ability to handle all of this task.

Enterprise Resource Planning (ERP) systems have emerged as essential tools in streamlining financial reporting and improving management efficiency across various industries, including large-scale energy operations. This theoretical review explores how ERP systems enhance financial reporting by integrating real-time data, automating financial processes, and fostering transparency. In energy operations, where financial activities are complex and data-intensive, ERP systems centralize financial data, reducing errors, improving compliance, and supporting decision-making through enhanced visibility and accuracy. By automating tasks such as budgeting, forecasting, and financial statement generation, ERP systems enable energy companies to efficiently manage their financial resources, thus optimizing cost control and profitability. Additionally, ERP systems provide significant benefits for management efficiency, particularly in large-scale operations. The integration of financial, operational, and managerial processes within a single platform allows for better resource allocation, improved communication across departments, and faster response times to market fluctuations. These capabilities are crucial in the energy sector, where companies face volatile market conditions, regulatory pressures, and high operational risks. ERP systems enhance managerial decision-making by providing real-time insights into key performance indicators (KPIs), enabling executives to make data-driven decisions that support long-term strategic goals. However, implementing ERP systems in large-scale energy operations presents challenges, including high costs, lengthy implementation periods, and the need for employee training. This review also examines these barriers and suggests strategies for successful ERP adoption, including phased implementation, stakeholder engagement, and customization to industry-specific needs. In conclusion, ERP systems hold the potential to revolutionize financial reporting and management efficiency in large-scale energy operations. By leveraging ERP's real-time data integration and process automation, energy companies can significantly improve their financial management, enhance decision-making, and adapt more quickly to market changes. Keywords: ERP Systems, Financial Reporting, Management Efficiency, Large-Scale Energy Operations, Process Automation, Real-Time Data Integration, Decision-Making, Cost Control, Market Fluctuations, Energy Sector.

This article comprehensively analyzes event-driven data pipelines in cloud computing environments, examining their architecture, implementation considerations, and real-world applications. The article explores the fundamental components of event-driven systems, from event sources and message brokers to processing layers, while evaluating their performance characteristics and reliability mechanisms. Through detailed analysis of system architectures, we investigate the integration of various cloud services and their role in enabling scalable, real-time data processing. The article demonstrates how modern event-driven architectures achieve sub-millisecond processing times and handle millions of events per second while maintaining system resilience. Our findings reveal significant improvements in operational efficiency across various industries, including financial services, marketing automation, and IoT monitoring solutions. The article also addresses critical implementation aspects, including security frameworks, integration protocols, and best practices for system optimization. These insights provide valuable guidance for organizations leveraging event-driven architectures in their digital transformation initiatives. The article concludes that event-driven data pipelines represent a crucial advancement in data processing technology, offering unprecedented capabilities in handling real-time data streams while maintaining scalability, reliability, and cost-effectiveness.