Data Management System Research Articles

Artificial intelligence detection system based on multi-sensor and wireless communication

In today’s world, Internet technology and wireless communication technology are becoming more and more mature, which has brought massive network information resources to all sectors of society. At the same time, the phenomenon of data loss caused by illegal network intrusion is becoming more and more common. Therefore, it is necessary to identify and deal with them in combination with IDS (intrusion detection system). The problem of data processing is also very important. Enterprises can build a data management system according to their own needs, and use this system to process data. With the help of science and technology, AI (Artificial Intelligence) technology has become more mature and applied in many industries. Therefore, this paper proposed to build an AI IDS, and combined the deep RL (reinforcement learning) algorithm to analyze the performance of the system. This paper tested and analyzed the system from the aspects of precision and recall. The experimental results showed that the average precision of the five data sets was 94.76%, and the average recall rate was 91.4%. From the above data, combined with the algorithm in this paper, the precision and recall of the system have been significantly improved. This paper also conducted benchmark energy consumption comparison experiments for different cloud data management systems. The results showed that in terms of loading, the benchmark energy consumption of HBase was the lowest, which was 86KJ. In terms of query, the benchmark energy consumption of GridSQL was the lowest, which was 56KJ. It can be seen that different systems have their own advantages in the benchmark energy consumption of loading and query.

Setting up an institutional OMERO environment for bioimage data: Perspectives from both facility staff and users.

Modern bioimaging core facilities at research institutions are essential for managing and maintaining high-end instruments, providing training and support for researchers in experimental design, image acquisition and data analysis. An important task for these facilities is the professional management of complex multidimensional bioimaging data, which are often produced in large quantity and very different file formats. This article details the process that led to successfully implementing the OME Remote Objects system (OMERO) for bioimage-specific research data management (RDM) at the Core Facility Cellular Imaging (CFCI) at the Technische Universität Dresden (TU Dresden). Ensuring compliance with the FAIR (findable, accessible, interoperable, reusable) principles, we outline here the challenges that we faced in adapting data handling and storage to a new RDM system. These challenges included the introduction of a standardised group-specific naming convention, metadata curation with tagging and Key-Value pairs, and integration of existing image processing workflows. By sharing our experiences, this article aims to provide insights and recommendations for both individual researchers and educational institutions intending to implement OMERO as a management system for bioimaging data. We showcase how tailored decisions and structured approaches lead to successful outcomes in RDM practices.

FORECASTING CONSUMER PRICE INDEX USING MACROECONOMIC VARIABLES: A COMPARATIVE ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING APPROACHES

The Turkish economy has faced many economic difficulties throughout it's history. At this point, predicting inflation accurately is very important for policy makers, businesses, investors and consumers. This study aims to estimate the Turkish Consumer Price Index. Producer price index, M1 money supply, gold price, dollar price, natural gas price and interest rate variables were used to estimate the CPI for Turkey. The variables used in the research were obtained through EVDS, the Central Bank's Electronic Data Management System. Monthly data from January 2003 to August 2023 was used in the study. The obtained data were estimated using DDPG, XGBoost, SVR, KNN and CNN-BiLSTM methods. Model performances were compared using RMSE, MSE, MAE, MAPE and R2 statistical coefficients. When model performances were evaluated, the best CPI prediction for Turkey was obtained by the SVR method.

Rapid in-field soil analysis of plant-available nutrients and pH for precision agriculture—a review

AbstractThere are currently many in-field methods for estimating soil properties (e.g., pH, texture, total C, total N) available in precision agriculture, but each have their own level of suitability and only a few can be used for direct determination of plant-available nutrients. As promising approaches for reliable in-field use, this review provides an overview of electromagnetic, conductivity-based, and electrochemical techniques for estimating plant-available soil nutrients and pH. Soil spectroscopy, conductivity, and ion-specific electrodes have received the most attention in proximal soil sensing as basic tools for precision agriculture during the last two decades. Spectral soil sensors provide indication of plant-available nutrients and pH, and electrochemical sensors provide highly accurate nitrate and pH measurements. This is currently the best way to accurately measure plant-available phosphorus and potassium, followed by spectral analysis. For economic and practicability reasons, the combination of multi-sensor in-field methods and soil data fusion has proven highly successful for assessing the status of plant-available nutrients in soil for precision agriculture. Simultaneous operation of sensors can cause problems for example because of mutual influences of different signals (electrical or mechanical). Data management systems provide relatively fast availability of information for evaluation of soil properties and their distribution in the field. For rapid and broad adoption of in-field soil analyses in farming practice, in addition to accuracy of fertilizer recommendations, certification as an official soil analysis method is indispensable. This would strongly increase acceptance of this innovative technology by farmers.

Data Management System and Software for Comprehensive Assessment of the State of the Biosphere

Data Management System and Software for Comprehensive Assessment of the State of the Biosphere

Fine-grained vulnerability detection for medical sensor systems

The Internet of Things (IoT) has revolutionized the healthcare system by connecting medical sensors to the internet, while also posing challenges to the security of medical sensor networks (MSN). Given the extreme sensitivity of medical data, any vulnerability may result in data breaches and misuse, impacting patient safety and privacy. Therefore, safeguarding MSN security is critical. As medical sensor devices rely on smart healthcare software systems for data management and communication, precisely detecting system code vulnerabilities is essential to ensuring network security. Effective software vulnerability detection targets two key objectives: (i) achieving high accuracy and (ii) directly identifying vulnerable code lines for developers to fix. To address these challenges, we introduce Vulcoder, a novel vulnerability-oriented, encoder-driven model based on the Bidirectional Encoder Representations from Transformers (BERT) architecture. We propose a one-to-one mapping function to capture code semantics through abstract syntax trees (AST). Combined with multi-head attention, Vulcoder achieves precise function- and line-level detection of software vulnerabilities in MSN. This accelerates the vulnerability remediation process, thereby strengthening network security. Experimental results on various datasets demonstrate that Vulcoder outperforms previous models in identifying vulnerabilities within MSN. Specifically, it achieves a 1%–419% improvement in function-level prediction F1 scores and a 12.5%–380% increase in line-level localization precision. Therefore, Vulcoder helps enhance security defenses and safeguard patient privacy in MSN, facilitating the development of smart healthcare.

Sistem Manajemen Kepegawaian dan Pencatatan Pekerja Migran Indonesia di BP3MI Riau dengan Metode Prototipe

Balai Pelayanan Pelindungan Pekerja Migran Indonesia (BP3MI) Riau, a government agency dedicated to protecting Indonesian migrant workers. The agency faces challenges with its unstructured and inefficient data management system. The use of Microsoft Excel for documenting the data of Indonesian Migrant Workers (PMI) and employees at BP3MI Riau is prone to data entry errors, time inefficiencies, unstructured data, potential damage, limited access, restricted analysis, difficulties in tracking changes, and a high risk of data loss. To address these issues, research was conducted to develop a more effective employee management and PMI data recording system using the prototype method. This method emphasizes user collaboration in understanding system requirements, resulting in practical solutions. The prototype method stages include user requirements gathering, designing input, process, and output needs, followed by rapid UML model design, program code implementation, and finally, testing. The system development results show that users have a very positive impression from the usability testing, achieving a score of 97.76%. This indicates the effectiveness of the prototype. The system significantly improves manual methods, enhancing the efficiency, accuracy, and accessibility of data at BP3MI Riau, and lays the foundation for future advancements with greater integration and automation prospects in BP3MI Riau's operations.

Optimalisasi Dashboard Pemesanan Makanan Online Menggunakan Looker dan JavaScript

Advances in information technology have enabled rapid development in internet-based services, including online food ordering applications. This growth demands efficient data management and analysis systems to improve user experience and operational performance. This research focuses on developing an optimal online food ordering dashboard using Looker and JavaScript. Research methods include needs identification, literature study, needs analysis, design, and implementation. The research results show that the dashboard developed is able to manage and analyze order data effectively, identify trends, predict customer needs, and increase operational efficiency. This dashboard visualizes important information such as number of orders based on age, gender, income, as well as customer behavior analysis. In doing so, service providers can gain better insight into consumer behavior and ordering trends, supporting more informed and strategic decision making. The results of this study contribute to the literature on the use of data visualization technologies in the online food service sector.

Retrospective analysis of the resuscitation room management of nontraumatic critically ill children in a university emergency department (OBSERvE-DUS-PED study)

The establishment of a resuscitation room management for nontraumatic critically ill children appears to make sense. This study collected data of pediatric patients suffering from nontraumatic critically ill conditions treated in aresuscitation room. The retrospective OBSERvE-DUS-PED study (November 2019-October 2022) recorded pediatric patients (age < 18years) who were admitted to the emergency department (ED) for resuscitation room care. The routinely documented data on treatment were taken from the hospital information system MEDICO® and the patient data management system COPRA® in accordance with the OBSERvE dataset. The study was approved by the Ethics Committee of the Medical Faculty of the Heinrich Heine University (2023-2377). The study included 52pediatric resuscitation room patients. Adolescents aged 14-17years were the most frequent in the cohort representing 37% of the total and neonates/infants (0-1year) were lowest at 8%. The most common symptoms categorized according to ABCDE problems were disturbance of consciousness(D) at 61%, cardiovascular failure(C) at 25%, respiratory insufficiency(B) at 6%, airway obstruction(A) and exposure/environment (E) problems each at 4%. The out-of-hospital and in-hospital emergency procedures were performed with the following frequencies: venous (58%vs. 65%), intraosseous (14%vs. 2%) and central venous access (0%vs. 12%), invasive airway management (35% vs.8%), cardiopulmonary resuscitation (21%vs. 10%), vasopressors (15%vs. 17%), and intra-arterial pressure measurement (0%vs. 17%). The mean duration of resuscitation room management was 70 ± 43 min. The 30-day mortality was 17%. The OBSERvE-DUS-PED study demonstrates the major challenges in the care of critically ill nontraumatic pediatric patients, both in out-of-hospital and in-hospital management. The variety and complexity of the referral diagnoses as well as the immediate vital threat to the patients make it appear sensible to treat such patients primarily in aresuscitation room of the ED due to the available material, infrastructural and personnel resources.

Blockchain-based privacy preservation framework for preventing cyberattacks in smart healthcare big data management systems

Blockchain-based privacy preservation framework for preventing cyberattacks in smart healthcare big data management systems

Secured Public Health data Management Framework using the Internet of Things in a Smart City Environment

The Internet of Things (IoT) is being embraced and impacts various aspects of lives. Integrating IoT technology in the healthcare sector paves the way for continuous remote monitoring of individuals' health status. The healthcare (HC) sector has traditionally produced substantial data. Due to the implementation of the IoT, this quantity has significantly increased. Ensuring proper HC storage is imperative to derive meaningful insights from the data. This study presents a public health (PH) framework that utilizes cloudlets and IoT technology. The primary objective of this PH architecture is to facilitate the retrieval of real-time data by using cloudlets. The research provides and executes an HC data management system to store vast HC data and handle consumer queries to retrieve HC information. The efficacy of the proposed model has been evaluated in terms of data transmission time, energy usage, query response time, and data packet loss. The proposed model outperforms existing PH systems by comparing the assessment findings of the proposed approach with the performance of typical PH systems.

The Role of Financial Data Processing and Application in Strengthening National Financial Supervision

This study focuses on the role of financial data processing and application in strengthening national financial supervision. It begins by discussing the background and significance, purpose and methods of studying this topic in the digital age. The theoretical foundations of financial data processing and application are introduced in detail, including the concept and characteristics of financial data, related theories of data processing and application, and the objectives and principles of financial supervision. The current status and challenges of financial data utilization in national financial supervision are analyzed. The multifaceted role of financial data processing and application in strengthening financial supervision is explored in depth, such as improving the transparency of fiscal fund use, accurately identifying problems and risks, optimizing budget management, and improving the performance evaluation system. Strategies for using financial data analysis to strengthen budget management and performance evaluation are proposed, including establishing and improving financial data collection and management systems, using data analysis tools for budget management, and building data analysis models for performance evaluation. The importance of financial data in maintaining national financial security and stability is emphasized, including preventing financial risks and ensuring sustainable fiscal development. Experiences, lessons, and insights are summarized and enlightened through the analysis of successful and problematic cases. Finally, the study is concluded with an outlook on the future application of financial data in financial supervision, including aspects such as technological development, system construction, talent cultivation, and data sharing.

PENDEKATAN FRAMEWORK CODEIGNITER DENGAN METODE AGILE PADA SISTEM PENGGAJIAN (SIP-WEB) PT. GLOBAL PRINTPACK INDONESIA

PT. Global Printpack Indonesia is engaged in plastic factories located in Curug District, Tangerang Regency. The company has 280 employees. With the employee payroll data management system that is already running, it uses Microsoft Excel which is taken from the fingerprint attendance data recap. So that during the HRD payroll process, it takes longer to calculate salaries. Therefore, a computer-based payroll system was developed with a data import feature. The goal is to help simplify the process of recording overtime hours, monitoring employee attendance more efficiently. So HRD does not need to calculate manually. The data collection methods used in the preparation of the thesis are observation, interviews and literature studies and agile software development methods using the codeigniter 3 framework. The existence of this payroll system can make it easier for HRD to calculate salaries automatically to increase efficiency

Road traffic crash data in Southwestern Nigeria: a qualitative analysis of sources, contents and management methods

Objective This study qualitatively examined road traffic crash (RTC) data collection and management in southwest Nigeria, with the goal of addressing the tenets of the UN Decade of Action Plan on Road Safety’s call for an effective data system. Methods Data on RTCs data collection in the study area was obtained through key informant interviews with five hospital accident emergency unit directors, three Federal Road Safety Commission (FRSC) Sector commanders, and three Nigeria Police Force (NPF) commissioners. Datasets were transcribed, categorized, and interpreted using content analysis, descriptive statistics, and WHO recommended minimal crash data element requirements. Results The study found disparate systems, a lack of synergy, and discrepancy in the various data sources in the country when compared with WHO recommendations on road traffic data collection systems, with 55% of agencies using non-standardized paper forms. The study also reveals that the minimum crash data elements recommended by WHO are not consistently captured in the NPF, FRSC, and hospital traffic crash data sources. Conclusions The study suggested the use of an upgraded National Road Traffic Crash Data Management System (NRTCDMS) Data Template to unify data gathering and linkage issues, but recommends a digital version of the template.

Community Nursing in the digital age, utilizing Technology to improve health Services

This research explores how digital technologies are utilized in community nursing to improve the quality of health care. Through the literature review method, this research analyzes various sources that discuss the use of technologies such as telehealth, health apps, cloud-based data management systems, and wearable devices. The results showed that digital technologies can improve accessibility, efficiency, and coordination in community healthcare, especially for populations living in remote areas. However, the study also identified challenges such as data privacy and security concerns, technology infrastructure gaps, and low digital literacy in some communities. Policy support, training programs, and culturally sensitive approaches are seen as key factors to overcome these challenges and maximize the benefits of technology in community nursing. This study concludes that digital technology has great potential to empower community nurses to provide better care and contribute to improving people's quality of life, provided that the challenges can be overcome through appropriate strategies and adequate support.

Implementation of the Naive Bayes Method in Looker Studio for data on the achievement of Great IDN in IDN Akhwat School

The IDN Hebat program is an important tool for schools to track and analyze student achievement data. However, with the targeted activities in the IDN program, challenges arise in managing and measuring achievement data efficiently. The research aims to develop a Web Cloud-based data management system for IDN Hisbat achievements at IDN Akhwat School by utilizing Google Looker Studio and the Naive Bayes Algorithm. The data source used in this study is by applying a classification dataset obtained from student achievement information data in the Great IDN Program. The results of this analysis show that the highest accuracy of teaching achievement fell on the status of exceeding the target with a percentage of 89%, and the highest class that placed the status above the target was class 9A with an average percentage of 35%. In addition, the results from this analysis can help coordinators and schools in planning more effective and strategic programs in the future. Overall, this study provides important benefits in improving the quality of teaching and student coaching, as well as supporting data-driven decision-making. This study is expected to enhance the efficiency, accuracy, and effectiveness of managing student achievement, while also supporting the attainment of optimal educational goals for each student to achieve extraordinary results.

Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics

Burnout and workforce attrition present pressing global challenges in healthcare, severely impacting the quality of patient care and the sustainability of health systems worldwide. Artificial intelligence (AI) has immense potential...

Using Health IT Standards to Facilitate Metadata Exchange Between Research Data Management Systems.

The National Research Data Infrastructure for Personal Health Data (NFDI4Health) uses Local Data Hubs (LDHs) to manage locally research studies, documents and sensitive personal data to support controlled data sharing. While research data management (RDM) systems facilitate the storage and preparation of data and metadata as well as organizational access, they often lack support for interoperability standards of the application domain. To support the exchange with external registries of research studies, we chose 17 attributes to characterize the most relevant aspects of clinical trials (in the following named "metadata profile"). We implemented the metadata profile in the RDM system FAIRDOM SEEK using core attributes and SEEK's extended metadata feature and created a mapping conforming to the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) standard version R4. Finally, we implemented a prototype application interface for exports in FHIR-JSON format. We plan to extend the interface to serve central registries and support specific FHIR Implementation Guides from various use cases.

Potential use of Six Sigma metrics in the quality control review of hospital glucose meters

Background and aimThis study applied Six Sigma metrics to facilitate the quality control (QC) review for hospital glucose meters. Materials and methodsQC data from a period of six months on all hospital glucose meters were extracted from the data management system. Sigma values for each meter at two QC levels were calculated and evaluated each month by combining the imprecision, the absolute bias between the meter mean and all-meter mean, and the standards from ISO 15179:2013. The effectiveness of using Sigma values in identifying meters with possible quality problems for further Levey-Jennings QC chart review was assessed. ResultsMore than 80 % of the meter's Sigma values within the six months were greater than 4 at either QC level. At the high QC level, twice as many Sigma values were below 4 than the low QC level. Including Sigma values 4, 3.5 or 3 in the criteria for the QC review reduced the number of chart review to 32.8 %, 11.2 % or 3.5 %, respectively. ConclusionsThe majority of the glucose meters examined in this study demonstrated optimal Sigma values. The Sigma metrics-based approach could be a valuable tool to guide an effective QC review of glucose meters for quality improvement.

A Framework to Support the Standardized Management of Real-World Data.

Relying on our experience on the development of data registration and management systems for clinical and biological data coming from patients with hematological malignancies, as well as on the design of strategies for data collection and analysis to support multi-center, clinical association studies, we designed a framework for the standardized collection and transformation of clinically relevant real-world data into evidence, to meet the challenges of gathering biomedical data collected during daily clinical practice in order to promote basic and clinical research.