Data Retrieval Time Research Articles

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

Electrical Energy Monitoring and Analysis System at Home using IoT-based Prophet Algorithm

Electrical energy is one of the necessities of human life, especially in modern society in urban areas. With a monitoring device for electrical energy consumption using IoT technology, the results of the development show that the monitoring system works well, but the results show that current and voltage measurements are still less accurate. Therefore, in this study, an Electrical Energy Analysis and Monitoring System were developed using the IoT-Based Prophet Algorithm. Data collection was obtained from electrical energy using the PZEM-004T module sensor device used at home and the energy data obtained were stored in a MySQL database. This PZEM data retrieval will appear in real time on the Monitoring Website. The dataset was processed by implementing the Prophet Algorithm, evaluating the model and visualizing the prediction results on the analysis website. Testing using Mean Absolute Percentage Error (MAPE). For design, this system uses energy data and data retrieval time as parameters in the monitoring system for the use of electrical energy at home. Analysis of data taken from electrical energy monitoring was predicted by the model created by the Prophet Algorithm and tested with MAPE to see how accurate the predicted value is in the Prophet Algorithm model. Predictions in this study get an error value of less than 10%, namely 6.87%, which means it is very accurate in predicting the prophet algorithm at home.

Enhancing data retrieval efficiency in large-scale JavaScript object notation datasets by using indexing techniques

<div class="translate-tooltip-mtz green sm-root translate hidden_translate"><div class="header-wrapper"><div class="header-controls"><span>The use of JavaScript Object Notation (JSON) format as a Not only Structured Query Language (NoSQL) storage solution has grown in popularity, but has presented technical challenges, particularly in indexing large-scale JSON files. This has resulted in slow data retrieval, especially for larger datasets. In this study, we propose the use of JSON datasets to preserve data in resource survey processes. We conducted experiments on a 32-gigabyte dataset containing 1,000,000 transactions in JSON format and implemented two indexing methods, dense and sparse, to improve retrieval efficiency. Additionally, we determined the optimal range of segment sizes for the indexing methods. Our findings revealed that adopting dense indexing reduced data retrieval time from 15,635 milliseconds to 55 milliseconds in one-to-one data retrieval, and from 38,300 milliseconds to 1 millisecond in the absence of keywords. In contrast, using sparse indexing reduced data retrieval time from 33,726 milliseconds to 36 milliseconds in one-to-many data retrieval and from 47,203 milliseconds to 0.17 milliseconds when keywords were not found. Furthermore, we discovered that the optimal segment size range was between 20,000 and 200,000 transactions for both dense and sparse indexing.</span></div></div></div>

REAL TIME DATA RETRIEVAL AND CONCURRENT DATA FLOW

Real -Time analytics (RTA) has emerged as a distinct branch of big data analytics focusing on the velocity aspect of big data , in which data is prepared , processed and analyzed as it arrives, intending to generate insight and create business value in near real-time. The software system being produced is called E-Commerce Web. This system is designed to “Provide Real Time data Retrieval & Management” for the process of placing an order on the Internet and facilitating the actual delivery of the product. E-Commerce is now seen as a reality for many businesses and a normal part of a business plan. Different key technologies used and highlights the significant features including real time data update via php, Inventory Management and Security, Employee Management and Attendance Functionality, User Registration and related issues, Payment options, Mobile Functionalities are considered. We proposed a system here consumer moves through the internet to the web site. From there, he decides that he wants to purchase something, so he is moved to the online transaction server, where all of the information he gives is encrypted. Once he has placed his order, the information moves through a private gateway to a Processing Network, where the issuing and acquiring banks complete or deny the transaction. This generally takes place in no more than 5-7seconds. It provides a brief overview of what e-commerce website business is about , its key features and the goals it aims to achieve. Key Words: Web Development, Technologies, E-Commerce, Real-Time Analytics, data streaming, Big data analytics, Data streaming, complex event processing.

Correlation Aware Relevance-Based Semantic Index for Clinical Big Data Repository.

In this paper, we focus on indexing mechanisms for unstructured clinical big integrated data repository systems. Clinical data is unstructured and heterogeneous, which comes in different files and formats. Accessing data efficiently and effectively are critical challenges. Traditional indexing mechanisms are difficult to apply on unstructured data, especially by identifying correlation information between clinical data elements. In this research work, we developed a correlation-aware relevance-based index that retrieves clinical data by fetching most relevant cases efficiently. In our previous work, we designed a methodology that categorizes medical data based on the semantics of data elements and merges them into an integrated repository. We developed a data integration system for medical data sources that combines heterogeneous medical data and provides access to knowledge-based database repositories to different users. In this research work, we designed an indexing system using semantic tags extracted from clinical data sources and medical ontologies that retrieves relevant data from database repositories and speeds up the process of data retrieval. Our objective is to provide an integrated biomedical database repository that can be used by radiologists as a reference, or for patient care, or by researchers. In this paper, we focus on designing a technique that performs data processing for data integration, learn the semantic properties of data elements, and develop a correlation-aware topic index that facilitates efficient data retrieval. We generated semantic tags by identifying key elements from integrated clinical cases using topic modeling techniques. We investigated a technique that identifies tags for merged categories and provides an index to fetch data from an integrated database repository. We developed a topic coherence matrix that shows how well a topic is supported by a corpus from clinical cases and medical ontologies. We were able to find more relevant results using an annotation index from an integrated database repository, and there was a 61% increase in a recall. We evaluated results with the help of experts and compared them with naive index (index with all terms from the corpus). Our approach improved data retrieval quality by providing most relevant results and reduced data retrieval time as we applied correlation-aware index on an integrated data repository. Topic indexing approach proposed in this research work identifies tags based on a correlation between different data elements, improves data retrieval time, and provides most relevant cases as an outcome of this system.

The Assessment of the Usability of Health Information Systems in Referral Hospitals in Tanzania: A Case of Geita Regional Referral Hospital

This study assesses the usability of the health information system in referral hospitals in Tanzania. The study divided the research participants into two strata, including service providers and service recipients, to ensure balanced opinions. The participants were obtained using a simple random sampling technique. The mixed-research approach was applied to collecting both quantitative and qualitative data. A questionnaire and an unstructured interview guide were used in data collection. The findings revealed that the usability of the health information system at Geita regional referral hospital is adequate, despite a few insights that should be taken into consideration to maximize its usability, quality of service delivery, and increased level of acceptance. The usability factors that were perceived positively include visibility, navigation, and easy retrieval of data. On the other hand, although factors including task-technology match, report generation capability, stock level management notifications, and the ability to manage queues were positively perceived, it was found that a significant percentage of participants were not sure or disagreed. Additionally, despite the usability challenges, the use of the health information system at Geita Regional Referral Hospital has resulted in improved quality of healthcare provision in terms of reduced waiting time, easy and quick patient data retrieval, a proper payment system, and equitable service.

HealthBlock: A Blockchain-IoT Fusion for Secure Healthcare Data Exchange

Managing healthcare data while ensuring its security and privacy is critical to providing quality care to patients. However, traditional approaches to healthcare data sharing have limitations, including the risk of data breaches and the lack of privacy-preserving mechanisms. This research paper proposes a novel hybrid blockchain-IoT approach for privacy-preserving healthcare data sharing that addresses these challenges. Our system incorporates a private blockchain for protected and tamper-proof data sharing, with privacy-preserving techniques such as differential privacy and homomorphic encryption to protect patient data. IoT devices are utilized to collect and transmit real-time data, equipped with privacy-preserving mechanisms such as data anonymization and secure transmission protocols. Our approach achieved an accuracy rate of 98% for access control and a 99.6% success rate for data privacy protection. Furthermore, our proposed system demonstrated improved data storage and retrieval performance, with a data storage overhead reduction of up to 86% and a data retrieval time reduction of up to 81%. These results indicate the potential of our approach to enhance the security, privacy, and efficiency of healthcare data management, contributing to improved patient care outcomes.

Electronic Medical Records (EMR) Using a Software as a Service (SaaS) with a Single Identity Number at the Polije Polyclinic

Manual management of medical records at the Polije Polyclinic causes long data retrieval time, misfiling, and requiring a lot of spaces for storage, thus hampering health services. Electronic Medical Records (EMR) are one type of medical record management that can be used to solve the issue. EMR is built based on Software as a Service (SaaS) which enables it to be used by all healthcare providers with minimal infrastructure and optimal data integration. The application used a single identity number for each patient, and it caused less redundancy in patient registration data. Developed using the Waterfall method, it was processed at several stages: analysis, design, implementation, testing, deployment, and maintenance. The EMR system had some features: recording history, diagnosis, and therapy. In the diagnosis section, the system had a disease code related to ICD-10. It can be concluded that the Electronic Medical Record (EMR) is ready to be implemented in the POLIJE polyclinic. The implementation of EMR is important to reinforce the Regulation of the Indonesian Minister of Health Number 24 of 2022 concerning Medical Records.

An algorithm for retrieving aerosol optical depth from Landsat 8 Operational Land Imager in Vietnam

High-resolution aerosol products offer advantages over lower spatial-resolution products for monitoring pollution in urban areas and industrial zones. Therefore, in this study we develop a Landsat 8 algorithm to retrieve aerosol optical depth (AOD) based on the MODIS Dark Target (DT) algorithm. This algorithm uses the direct surface reflectance of Landsat 8 to build a surface reflectance relationship between the visible (BLUE, RED) and short-wave infrared bands (SWIR2). AOD data from AERONET stations in the study area and AOD data from MODIS 3 km were used for validation of the estimated Landsat 8 AOD. The estimated AOD was highly correlated at most AERONET AOD, with =0.98, (RMSE = 0.3), except Bac Lieu, but only had a moderate correlation with MODIS AOD which was attributed to the difference in data retrieval time and associated changes in climate conditions. This is the first high-resolution AOD retrieval in Vietnam, leading to more practical applications for air quality monitoring.

Sistem Monitoring dan Analisis Penggunaan Energi Listrik Rumah Berbasis Internet of Things Menggunakan Prophet Algorithm

Electrical energy is one of the necessities of human life, especially in modern society in urban areas. With a monitoring device for electrical energy consumption using IoT technology, the results of the development show that the monitoring system works well, but the results show that current and voltage measurements are still less accurate. Therefore, in this study, an Electrical Energy Analysis and Monitoring System was developed using the IoT-Based Prophet Algorithm. Data collection was obtained from electrical energy using the PZEM-004T module sensor device used at home and the energy data obtained were stored in a MySQL database. This PZEM data retrieval will appear in real-time on the Monitoring Website. The dataset was processed by implementing the Prophet Algorithm, evaluating the model and visualizing the prediction results on the analysis website. Testing using Mean Absolute Percentage Error (MAPE). For design, this system uses energy data and data retrieval time as parameters in the monitoring system for the use of electrical energy at home. Analysis of data taken from electrical energy monitoring was predicted by the model created by the Prophet Algorithm and tested with MAPE to see how accurate the predicted value is in the Prophet Algorithm model. Predictions in this study get an error value of less than 10%, namely 6.87%, which means it is very accurate in predicting the prophet algorithm at home.

A blockchain-enabled IoT auditing management system complying with ISO/IEC 15408-2

Recently, blockchain has attracted attention as one of the most promising key platform technologies for leveraging IoT (Internet of Things) security and auditing management with trusted and decentralized architecture. Therefore, the possibilities of using blockchain for providing scalable management, efficient access, and accountability for the massive numbers of extant and emerging IoT devices merits investigation. In this study, we implement a blockchain-enabled IoT auditing management system complying with ISO/IEC 15408-2 requirements. The implemented system is operated with a stable data retrieval time, i.e. 25.74 to 26.77 microseconds, and a stable compressing and uploading time, i.e. 32.51 and 39.9 microseconds when handling and accessing below 100 logs. With more logs, the processing time raises in a proportional manner. The introduced auditing system represents an evolutionary paradigm not only possessing the advantageous characteristics of blockchain technology, i.e. de-centralization, robustness and tamper-resistance, but also fulfilling the critical requirements from ISO/IEC 15408-2 with respect to auditing perspective. Moreover, the scalability is guaranteed, as our proposed IoT auditing management system is suitable for versatile IoT-oriented application services (even on heterogeneous platforms).

RANCANG BANGUN SISTEM INFORMASI PENDATAAN PKL BERBASIS FRAMEWORK CODEIGNITER 3

The purpose of this study is to build and implement a management information system for data collection of street vendors at DISKOMINFO, Bandung Regency
 The method used in the study uses a descriptive qualitative method, which contains data quotations revealed in the field to provide support for what is presented in the study. Data collection techniques used are direct observation observations in the field, interviews and literature studies.
 The problems that occurred in the study were PKL data collection which still uses a manual system which causes data not to be archived properly, sometimes there is data duplication, ineffective data retrieval time, and limited archive storage space.
 From the results of the study, to overcome the problems that occur in DISKOMINFO, it currently requires a PKL data collection information system as a solution to overcome the problem by creating a web application to provide PKL data collection information. The design of this application system uses the Codeigniter Framework 3 and the system uses MySQL database storage. With this application program, it makes it easier for users to find information about PKL and facilitates the pkl data collection process.

Optimal Performance of Security by Fragmentation and Replication of Data in Cloud

Abstract: Outsourcing data to a third-party administrative control, as is done in cloud computing, gives rise to security concerns. The data compromise may occur due to attacks by other users and nodes within the cloud. Therefore, high security measures are required to protect data within the cloud. However, the employed security strategy must also take into account the optimization of the data retrieval time. In this paper, we propose Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) that collectively approaches the security and performance issues. In the DROPS methodology, we divide a file into fragments, and replicate the fragmented data over the cloud nodes. Each of the nodes stores only a single fragment of a particular data file that ensures that even in case of a successful attack, no meaningful information is revealed to the attacker.

Performance and time improvement of LT code-based cloud storage

Outsourcing data on cloud storage services has already attracted great attention due to the prospect of rapid data growth and storing efficiencies for customers. The coding-based cloud storage approach can offer a more reliable and faster solution with less storage space in comparison with replication-based cloud storage. LT codes are the famous member of the rateless code family that can improve performance of storage systems utilizing good degree distributions. Since degree distribution plays a key role in LT codes performance, recently introduced Poisson robust soliton distribution (PRSD) and combined Poisson robust soliton distribution (CPRSD) motivate us to investigate LT code-based cloud storage system. So, we exploit LT codes with new degree distributions to provide lower average degree and higher decoding efficiency, specifically when receiving fewer encoding symbols, compared with popular degree distribution, robust soliton distribution (RSD). In this paper, we show that proposed cloud storage outperforms traditional ones in terms of storage space and robustness encountering unavailability of encoding symbols, due to compatible properties of PRSD and CPRSD with cloud storage essence. Furthermore, a modified decoding process is presented based on required encoding symbols behavior to reduce data retrieval time. Numerical results confirm improvement of cloud storage performance.

How to Get the Best Out of Your Fingerprint Database: Hierarchical Fingerprint Indoor Positioning for Databases With Variable Density

In this paper, we consider wireless positioning using Received Signal Strength (RSS) fingerprinting. To obtain good accuracy, this technique requires a database containing a high density of up-to-date fingerprints. However, as acquiring fingerprints through training is labor intensive and the indoor topology is subject to changes, a high density fingerprint database cannot always be obtained. On the other hand, the time to retrieve data from a database with high density can be too high for real-time positioning. To tackle these issues, we introduce the Hierarchical Positioning Algorithm (HPA). In this algorithm, we divide the database into a number of sub-databases with different densities, each containing a sufficiently small number of fingerprints to reduce the data retrieval time. The algorithm starts with a coarse estimate at the highest level, and gradually improves the accuracy in going to the lowest level. This HPA technique requires the construction of sub-databases containing fingerprints that are properly selected to obtain the wanted level of accuracy. This paper considers two algorithms to construct the database: the Minimum Distance Algorithm (MDA) to select the reference points, and the Local Gaussian Process (LGP) algorithm to determine the RSS values at the selected reference points. Simulation results show that the hierarchical algorithm, combined with MDA and LGP to construct the sub-databases, is a fast algorithm that can achieve high accuracy, even with a database having a variable density of fingerprints.

Knowledge, Perception, and Belief on Antibiotic Use in The Community of Panyabungan, Mandailing Natal District, Indonesia

Antibiotics are organic compounds produced by certain microorganisms and toxic to the other microorganisms. Irrational antibiotic use can cause serious health problems, especially bacterial resistance to antibiotics. This study aimed to determine the extent of knowledge, perception and belief on antibiotic use in Panyabungankota Subdistrict Mandailing Natal District. This study used cross-sectional design, total sample 198 people taken by purposive random sampling. Research data retrieval time during December 2017. In this study the questionnare used consisted of four sections they are demographic, knowledge, perceptions and belief. Analysis of the data used are univariate and bivariate analysis with chi-square method. From the result of the research, it was found that the public knowledge regarding antibiotic use was 37.9% belongs to good category, whereas society perception regarding antibiotic use was 69.7% belong to enough category and belief regarding antibiotic use was 74.7% belong to enough category. The result of statistical test showed significant relationship among public knowledge of antibiotic use and the characteristics of sex, age, last education, and work, p<0.1. The result of statistical test showed no significant relationship among perception and belief of antibiotic use and the characteristics of sex, age, last education, and work, p>0.1. and the result showed significant relationship between knowledge and beliefs, p<0.1, the result showed no significant relationship between knowledge and perception, p>0.1. and the result showed significant relationship between perception and beliefs, p<0,1. Based on the results of this study it can be concluded, that knowledge, perceptions and belief of antibiotic use still need to be improved.

ANTHROPOMETRIC IDENTIFICATION SYSTEM USING CONVOLUTION NEURAL NETWORK BASED ON REGION PROPOSAL NETWORK

The operating of the anthropometric identification supported byConvolution Neural Network system is to locate precious anthropology spots and certaindistances between each feature area on a person's face. Identifies the anthropometricpoints from 2D captured pictures by 3 perspective views promised an implementation between medical diagnostics to solve the problem of data retrieval time and efficiency compared to other manual measures.

Rancang Bangun Sistem Monitoring Suhu dan Salinias pada Air Laut

Knowing the condition of the waters is very important for the sustainability of aquatic ecosystems. The condition of the waters so as not to have a bad impact on aquatic ecosystems can be known through temperature and salinity parameters. Seawater temperature is the distribution of heat in the ocean caused by the movement of water and other factors. The salinity of seawater is the level of saltiness or the level of salt dissolved in seawater. Temperature can be measured using a thermometer and salinity is measured using a refractometer. However, using these tools requires time, effort, cost, and manual data retrieval processes. Therefore, this research aims to make a temperature and salinity measuring instrument and determine the relationship between Voltage and salinity in the seawater. This research used the method of measuring electrical conductivity (DHL) to determine a change in magnitude. The temperature and salinity design consisted of an Arduino Uno, a YL-38 Module, and copper as an electrode, and a DS18B20 Temperature Sensor. the result of this research shows that has been done, the average error presentation of salinity is 0.00732% and temperature is 0.044 %. Through this research, salinity and the temperature instrument produced using electrodes made of copper and DS18B20 which operate at a Voltage of 4,8 Volts. The obtained a linear graph between stress and salinity. The relationship between voltage and salinity is directly proportional, the higher the salinity of seawater, the greater the output voltage.

Application of Internet of Things and Edge Computing Technology in Sports Tourism Services

Aiming at the problems of long retrieval time of sports tourism data, high integration error of sports tourism data, and high energy consumption of sports tourism service management in traditional methods, the application of Internet of Things and edge computing technology in sports tourism services is proposed. We established a sports tourism service application model based on Internet technology to realize the functions of sports tourism service internal management control, external collaboration, and information release. We calculated the similarity of sports tourism resources from the two levels of feature words and the environment where the sports tourism resources are located. According to the calculation results, the edge computing method is used to realize the integration of sports tourism service resources to improve the application effect of sports tourism service application models. The experimental results show that the minimum data retrieval time of the proposed method is only 2.35s. The sports tourism data fusion error is low. The management energy consumption is small, which significantly improves the existing problems and fully verifies the practical application value of the method.

Telemedicine-equipped android interface-based heart rate monitoring

<p>This research aimed to design a device that can monitor heart rate and help nurses or doctors when they need to monitor and retrieve data of patients. By utilizing Android as a displayer makes it easier for nurses to minimize data retrieval time. The principle of the tool is to record the heart rate data received by the ear clip sensor to be processed by the Atmega328 microcontroller, then displayed on the oled LCD and sent to Android phones via HC-05 Bluetooth for display. If the heart rate data is beyond the normal range, the Android application will post a notification in the form of an SMS to the recipient's cellphone. In testing the tool, it uses a comparison device (pulse oximetry) to determine its accuracy. Based on the testing, the heart rate monitoring device had a small error value of 0.32% and had the most substantial error value of 0.81%. The application of the monitoring system in android data can be sent well at a maximum distance of 13 meters, as well as the implementation of telemedicine in the form of a warning (SMS) can work properly.</p>