Real-time Data Management Research Articles

Real-Time Industrial Data Analysis and Visualization

In the era of Industry 4.0, real-time data monitoring and analysis have become essential for enhancing operational efficiency and decision-making in industrial environments. This project focuses on the development of a comprehensive system for visualizing and analyzing real-time industrial data using a DHT11 sensor, which measures temperature and humidity. The collected data is transmitted and visualized through a dynamic web interface built on MyPHPAdmin, enabling users to access and interpret data seamlessly. The visualization aspect of the project aims to present the data in an intuitive format, allowing stakeholders to monitor environmental conditions effectively. Following the visualization phase, the data will undergo thorough analysis using advanced tools like Tableau and Excel, providing insights into patterns, trends, and anomalies in the industrial setting. This project not only demonstrates the integration of hardware and software technologies for real-time data management but also emphasizes the importance of data visualization and analysis in optimizing industrial processes. Ultimately, it serves as a foundation for further research and development in the field of industrial automation and data-driven decision-making.

The Role of Technology Integration in Public Sector Accounting Fraud Prevention Efforts

This research aims to determine the role of public-sector accounting information technology in preventing fraud in public-sector organizations. The study was a literature review based on articles suitable for further analysis of the research topic. The study found that there are ways to prevent fraud that, include technology and prevention strategies. The technology strategy includes leveraging data protection officers for fraud prevention, IoT access, data access management, timely reporting, and real-time data management. The prevention strategy is to develop a fraud detection system using machine algorithms, data mining, and meta-learning techniques.

Optimizing Business Operations through Edge Computing: Advancements in Real-Time Data Processing for the Big Data Era

The question of big data brought new opportunities and threats to business environment primarily in terms of processing increasingly large amounts of data in realtime. This paper examines the way in which edge computing can /should benefit business processes by offering improvements to real-time data analysis beyond what is provided by cloud-based systems while overcoming the challenge of latency, bandwidth and scalability. The study uses both primary and secondary data collected from 50 companies that use edge computing for processing of big data in industries including manufacturing, health, and retail. It involves cases where the factors are manifested, practices involving changes in numerical values of operations and other models involving the management of real-time data. Studies show that edge computing cuts down data processing delay by 40-60% on average, cuts data utilization and improves decision-making. This work also examines the use-cases of edge computing in order to demonstrate its potential across industries that must analyze data in near realtime, including self-driving automobiles, IoT gadgets, and logistics. Toward this end, this research provides an analysis of the ethical issues of decentralized data handling especially focusing on data security and data privacy. In response to the research questions of the paper, this study provides practical recommendations to help the companies in pursuing edge computing in the big data environment for the sake of sustaining market competitiveness. The research enriches the existing knowledge in business technology by presenting the possibility of change that edge computing brings.

Development of a Cloud Computing-Based Collaboration Information System

The rapid advancement of information and communication technology has created a demand for efficient and effective information systems to facilitate collaboration among institutions. This study develops a cloud computing-based collaboration information system designed to enhance real-time communication, data management, and collaboration among users. The system comprises key modules, including collaboration management, document management, and integrated collaboration features. Testing involved various scenarios with 10 to 100 active users simultaneously, revealing that the system maintained stable performance with response times under 3 seconds for each user request. Furthermore, security assessments utilizing encryption protocols and two-factor authentication confirmed that the managed data is protected from external threats. The findings underscore the system's potential to improve collaborative efficiency while ensuring data security, making it a valuable solution for future collaborative initiatives.

Open Source based Smart Meter Real-time Data Acquisition and Management using MQTT Protocol

Open Source based Smart Meter Real-time Data Acquisition and Management using MQTT Protocol

The Role of Multi-Agents in Digital Twin Implementation: Short Survey

In recent years, Digital Twin (DT) technology has emerged as a significant technological advancement. A digital twin is a digital representation of a physical asset that mirrors its data model, behaviour, and interactions with other physical assets. Digital Twin aims to achieve adaptability, seamless data integration, modelling, simulation, automation, and real-time data management. The primary goal of this paper is to explore the role of agents in DT implementations, seeking to understand their predominant usage scenarios and purposes. From our perspective, agents serving as intelligent entities play a role in realising the features of DTs. This paper also discusses the gaps in DT, highlights future directions, and analyses various technologies integrated with multi-agent systems technologies in DT implementations. Finally, the paper briefly discusses an overview of an architecture to implement a DT for smart agriculture with multi-agents.

Enhanced Pharmacy Service Delivery Through a Pharmacy Information System at a Medical Center: A Case Study.

A pharmacy information system (PIS) is a crucial tool for preventing errors and enhancing patient safety. This study aims to assess the influence of a PIS on workflow and medication errors (MEs) within the pharmacy department of a medical city in Saudi Arabia. The analysis encompasses an evaluation of the current workflow within pharmacy modules (in-patient, out-patient, and clinical pharmacy modules) and an exploration of associated challenges and inefficiencies. The study employed both qualitative and quantitative methodologies, incorporating structured interview techniques and the review of system records, such as logs, incident reports, and ME databases. Respondents recognized the benefits of the PIS, including the management of high-quality real-time data (26%), reduction of MEs (25%), acceleration of healthcare practices (23%), and absence of space-time constraints (14%). The study highlighted the role of PIS in addressing medication stock control and shortage issues, thereby improving overall medication availability. Furthermore, PIS significantly enhanced safety (93.3%), efficiency (80%), reliability, accuracy, ease of use (56.7%, 56.7%), and cost-effectiveness (53.7%). Following the implementation of the PIS, there was an increase in ME reporting, indicating improved error detection and management. The research revealed an increase in ME reporting rates in hospitals after PIS installation, attributed to improved medication management processes resulting in higher detection rates. As healthcare facilities increasingly embrace electronic medical records, the understanding of patient care quality will be significantly impacted. Consequently, further research is warranted to explore the role of PIS in reporting, detecting, analyzing, and mitigating MEs, as well as their influence on the work environment to promote a secure and efficient patient journey.

Reviewing the impact of artificial intelligence and machine learning in enhancing IoT applications performance

The convergence of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) has revolutionized various industries by enhancing IoT applications' efficiency and performance. This paper presents a comprehensive review of the impact of AI and ML in optimizing IoT systems. This study investigates how AI/ML algorithms contribute to real-time decision-making, energy efficiency, data management, and security in IoT networks through a qualitative research methodology. Additionally, it identifies the limitations of current research and provides directions for future work.

A Data Pipeline for Enhancing Quality of MAUDE-Based Studies.

The increasing use of MAUDE reports in patient safety research highlights the importance of understanding the processing and dissemination of open-access MAUDE data. However, the absence of a structured data pipeline undermines the reproducibility and transparency of studies relying on MAUDE data. In response, we conducted a comprehensive analysis of a recent study on endoscopic clips, assessing methodologies and results. We advocate for implementing an extract, transform, and load (ETL) pipeline, utilizing openFDA and integrating keyword search strategies and data visualization techniques. This approach aims to enhance the quality of MAUDE-based studies, ensuring their reproducibility and transparency. Moreover, ETL serves as a cornerstone in data engineering, enabling real-time data management and quality assurance, thus promoting the sustainability and collaboration of MAUDE-based patient safety research.

The Future of HCM: Moving to the Cloud

The landscape of Human Capital Management (HCM) is undergoing a significant transformation as organizations increasingly migrate their HCM systems to the cloud. Cloud-based HCM solutions offer unparalleled scalability, flexibility, and cost-efficiency, enabling businesses to streamline HR processes, enhance data accessibility, and leverage advanced analytics. This paper explores the future of HCM in the context of cloud migration, highlighting key drivers, benefits, and challenges associated with this shift. Through a comprehensive literature review and analysis of case studies from diverse industries, the study demonstrates how cloud-based HCM systems facilitate real-time data management, support remote and hybrid work models, and integrate seamlessly with other enterprise systems. Additionally, the research addresses critical concerns such as data security, privacy, and change management, proposing best practices for successful cloud adoption. The findings underscore the pivotal role of cloud computing in shaping the future of HCM, offering strategic insights for organizations aiming to optimize their human capital and maintain a competitive edge in a dynamic business environment.

PHREESQL: A toolkit to efficiently compute and store geochemical speciation calculation

Both abiotic and biotic natural spheres benefit from the high reactivity of natural waters, which are ubiquitous on planet Earth. The use of speciation-solubility codes like PHREEQC can provide a deeper understanding of aqueous equilibria and water-rock interactions. A significant number of newly derived variables are produced by these computations, which may significantly increase compared to input data. It is crucial to process vast amounts of data efficiently, particularly when dealing with datasets that contain thousands of water analyses.To tackle this problem, we present PHREESQL, a software package designed to efficiently store and manage extensive data generated by geochemical speciation computations performed by PHREEQC. High efficiency in data extraction and filtering of the entire output from a single run can be achieved through a well-designed relational SQL database structure. The PHREESQL can be used as a stand-alone package or embedded in third-party applications. Thanks to the SQL structure, it is possible to create links with unstructured meshes by developers and experts in reaction-transport problems. Real-time data management from multiparameter devices in field and laboratory settings is made possible and efficient by parallel computation options and software integration. The toolkit encompasses both a C++ library and a command-line interface, facilitating its use by geochemists with limited programming skills.

Design of Digital Twin Cutting Experiment System for Shearer.

This study presents an advanced simulated shearer machine cutting experiment system enhanced with digital twin technology. Central to this system is a simulated shearer drum, designed based on similarity theory to accurately mirror the operational dynamics of actual mining cutters. The setup incorporates a modified machining center equipped with sophisticated sensors that monitor various parameters such as cutting states, forces, torque, vibration, temperature, and sound. These sensors are crucial for precisely simulating the shearer cutting actions. The integration of digital twin technology is pivotal, featuring a real-time data management layer, a dynamic simulation mechanism model layer, and an application service layer that facilitates virtual experiments and algorithm refinement. This multifaceted approach allows for in-depth analysis of simulated coal cutting, utilizing sensor data to comprehensively evaluate the shearer's performance. The study also includes tests on simulated coal samples. The system effectively conducts experiments and captures cutting condition signals via the sensors. Through time domain analysis of these signals, gathered while cutting materials of varying strengths, it is determined that the cutting force signal characteristics are particularly distinct. By isolating the cutting force signal as a key feature, the system can effectively distinguish between different cutting modes. This capability provides a robust experimental basis for coal rock identification research, offering significant insights into the nuances of shearer operation.

AgroFarmIQ: Advanced Crop Monitoring and Yield Prediction

In response to the imperatives of modern agriculture, our project presents a comprehensive precision agriculture solution centered around a multifunctional robocar. This autonomous vehicle integrates IoT sensors for data-driven crop management, employing machine learning for monitoring and predicting yields while optimizing resource utilization. Automation features include automated obstacle avoidance, periodic image capture, and automatic water spraying for real-time data collection and management, empowering farmers with actionable insights for informed decision-making. Additionally, a web-based platform utilizing Streamlit framework facilitates rapid and accurate crop disease detection using a meticulously trained VGG19 model from uploaded images. By embracing advanced technology, our solution aims to enhance agricultural productivity and pave the way for a sustainable future. Keyword: Precision agriculture, Robocar, IoT sensors, Machine learning, Crop management, Automated farming, Data-driven agriculture, Resource optimization, Real-time monitoring, Sustainable farming

HydroRTC: A web-based data transfer and communication library for collaborative data processing and sharing in the hydrological domain

The exponential growth in data generated by satellites, radars, sensors, and analysis and reanalysis from model outputs for the hydrological domain requires efficient real-time data management and distribution mechanisms. This paper introduces HydroRTC, a web-based data transfer and communication library designed to accelerate large-scale data sharing and analysis. Leveraging next-generation web technologies like WebSockets, WebRTC and Node.js, the library enables seamless peer-to-peer sharing, smart data transmission, and large dataset streaming. Three primary scenarios are presented as use cases, demonstrating the potential of HydroRTC as server-to-peer with intelligent data scheduling and large data streaming, peer-to-peer data sharing, and peer-to-server for data exchange. HydroRTC offers a promising solution for collaborative infrastructures in the hydrological and environmental domain, allowing real-time and high-throughput data sharing and transfer for enhancing research efficiency and collaboration capabilities.

An Electronic Health Record-Integrated Application for Standardizing Care and Monitoring Patients With Autosomal Dominant Polycystic Kidney Disease Enrolled in a Tolvaptan Clinic: Design and Implementation Study.

Tolvaptan is the only US Food and Drug Administration-approved drug to slow the progression of autosomal dominant polycystic kidney disease (ADPKD), but it requires strict clinical monitoring due to potential serious adverse events. We aimed to share our experience in developing and implementing an electronic health record (EHR)-based application to monitor patients with ADPKD who were initiated on tolvaptan. The application was developed in collaboration with clinical informatics professionals based on our clinical protocol with frequent laboratory test monitoring to detect early drug-related toxicity. The application streamlined the clinical workflow and enabled our nursing team to take appropriate actions in real time to prevent drug-related serious adverse events. We retrospectively analyzed the characteristics of the enrolled patients. As of September 2022, a total of 214 patients were enrolled in the tolvaptan program across all Mayo Clinic sites. Of these, 126 were enrolled in the Tolvaptan Monitoring Registry application and 88 in the Past Tolvaptan Patients application. The mean age at enrollment was 43.1 (SD 9.9) years. A total of 20 (9.3%) patients developed liver toxicity, but only 5 (2.3%) had to discontinue the drug. The 2 EHR-based applications allowed consolidation of all necessary patient information and real-time data management at the individual or population level. This approach facilitated efficient staff workflow, monitoring of drug-related adverse events, and timely prescription renewal. Our study highlights the feasibility of integrating digital applications into the EHR workflow to facilitate efficient and safe care delivery for patients enrolled in a tolvaptan program. This workflow needs further validation but could be extended to other health care systems managing chronic diseases requiring drug monitoring.

Research on Detection System of Intelligent Computer Optimization Algorithm for Concrete Mix Ratio Based on Big Data

PLC is used to complete the control function of the concrete proportioning system. The MCGS (Configuration Software Monitor and Control System) is used to realize the real-time monitoring, data management and fault warning of the whole monitoring system. The material feed and PLC control process in the feed system are emphatically introduced. A concrete proportioning design method based on multiple parameters is proposed. The nonlinear multi-objective optimization model of concrete mix ratio was established by stepwise regression analysis and complex optimization method. The problems of narrow range of variables and single form of objective and constraint in traditional linear optimization methods are solved, which makes the selection of optimal index simpler and more flexible. Through the calculation of examples, it is clear that this method can obtain the mixture that meets the use conditions, and can greatly save the experimental work and construction cost.

Data pipeline for real-time energy consumption data management and prediction.

With the increasing utilization of data in various industries and applications, constructing an efficient data pipeline has become crucial. In this study, we propose a machine learning operations-centric data pipeline specifically designed for an energy consumption management system. This pipeline seamlessly integrates the machine learning model with real-time data management and prediction capabilities. The overall architecture of our proposed pipeline comprises several key components, including Kafka, InfluxDB, Telegraf, Zookeeper, and Grafana. To enable accurate energy consumption predictions, we adopt two time-series prediction models, long short-term memory (LSTM), and seasonal autoregressive integrated moving average (SARIMA). Our analysis reveals a clear trade-off between speed and accuracy, where SARIMA exhibits faster model learning time while LSTM outperforms SARIMA in prediction accuracy. To validate the effectiveness of our pipeline, we measure the overall processing time by optimizing the configuration of Telegraf, which directly impacts the load in the pipeline. The results are promising, as our pipeline achieves an average end-to-end processing time of only 0.39 s for handling 10,000 data records and an impressive 1.26 s when scaling up to 100,000 records. This indicates 30.69-90.88 times faster processing compared to the existing Python-based approach. Additionally, when the number of records increases by ten times, the increased overhead is reduced by 3.07 times. This verifies that the proposed pipeline exhibits an efficient and scalable structure suitable for real-time environments.

Enhancing Public Transit System Through AI and IoT

The paper investigates the synergy between AI and IoT in revolutionizing public transit systems, emphasizing their role in addressing existing challenges and improving overall efficiency. It explores the application of IoT devices in creating a smart infrastructure that enables real-time data collection, monitoring, and management of transit operations. The research delves into the realm of predictive maintenance, showcasing how AI algorithms, powered by IoT data, can anticipate potential issues in transit vehicles. By analyzing sensor data, transit authorities can proactively address maintenance needs, minimizing downtime, reducing repair costs, and ensuring a more reliable and sustainable public transit service. Route optimization emerges as another crucial aspect of the study, highlighting how AI algorithms leverage historical and real-time data to recommend the most efficient transit routes. Factors such as traffic patterns, weather conditions, and passenger demand are considered to enhance overall system efficiency and reduce travel time for passengers. The paper introduces the concept of dynamic scheduling, illustrating how AI-driven algorithms adapt transit schedules in real-time based on changing passenger needs and external factors. This dynamic approach aims to provide more responsive services, ultimately reducing wait times and improving overall user satisfaction. Passenger information systems are explored as a pivotal component, illustrating how AI and IoT technologies enhance the passenger experience. Real-time communication through mobile apps, digital displays, and other channels ensures that passengers have accurate and timely information about arrival times, delays, and alternative routes, empowering them to make informed decisions. The researchers also delve into fare optimization, examining how AI algorithms analyze data on passenger demographics, travel patterns, and economic factors to create fair and affordable fare structures. This approach aims to encourage ridership, increase revenue, and improve the financial sustainability of public transit systems. The abstract presents a comprehensive overview of how the integration of AI and IoT technologies in public transit systems transforms urban mobility. The findings suggest that leveraging real-time data, predictive analytics, and dynamic solutions can significantly enhance the reliability, accessibility, and sustainability of public transit. As cities continue to explore innovative solutions, the abstract serves as a roadmap for developing smarter, user-friendly, and efficient urban transportation networks, ultimately contributing to improved quality of life for residents.

Decentralizing spatial data: the convergence of Geographic Information Systems and Web 3.0 technologies


 
 
 Geographic Information Systems (GIS) have long served as pivotal tools for map- ping and understanding spatial relationships. However, the dawn of Web 3.0 technologies has catalyzed a transformative shift in the geospatial domain. This research meticulously investigates this transformation, focusing on the integration of decentralized spatial data storage, blockchain, Artificial Intelligence (AI), and Machine Learning (ML) with traditional GIS frameworks. Decentralized systems, un- derpinned by the principles of Web 3.0, present a promising alternative to centralized data storage, addressing challenges related to scalability, data sovereignty, and system vulnerabilities. Blockchain technology, traditionally associated with financial transactions, emerges as a cornerstone in this new GIS paradigm, ensuring unparalleled data integrity, transparency, and security. Its decentralized ledger system, combined with consensus mechanisms, offers a robust and transparent framework for managing diverse spatial data- sets, ranging from land registries to intricate environmental monitoring systems. The incorporation of AI and ML technologies further augments the capabilities of GIS. Beyond mere visualization, GIS, when powered by AI and ML, can process vast datasets, discern complex patterns, and even predict future spatial trends with remarkable accuracy. This research emphasizes the role of real-time and dynamic queries, highlighting the transition from static GIS analyses to more adaptive and predictive geospatial evaluations. While the potential advantages of this technological convergence are substantial, the research also sheds light on inherent challenges, espe- cially those related to the management of high-volume real-time data and ensuring data consistency across diverse sources. Building upon foundational works in the field, this study offers a holistic and comprehensive perspective on the synergistic potential of Web 3.0 technologies, AI, ML, and blockchain within GIS. It not only extends the findings of prior research but also paves the way for future explorations, setting the stage for innovative advancements in geospatial analysis.
 
 

Autonomous millimeter scale high throughput battery research system

The high-throughput Auto-MISCHBARES platform streamlines reliable autonomous experimentation across laboratory devices through scheduling, quality control, live feedback, and real-time data management, including measurement, validation and analysis.