Real Database Research Articles

A new electronic medical record database linked to claims data and discharge abstract data (the RWD database) in Japan: Study design and profile.

This article aims to introduce the Real World Database-a new clinical database in Japan. The Health, Clinic, and Education Information Evaluation Institute and Real World Data Co., Ltd. began developing the Real World Database in 2015. This is an electronic medical record database linked to claims data and discharge abstract data from medical institutions in Japan. The institutions agreed to collect data from 218 medical institutions as of June 2021. In 2019, 82 medical institutions provided data, which showed that 2,184,666 patients received treatment at medical institutions. There were also 334,437 inpatients with at least one hospital stay and 2,011,628 outpatients with at least one visit. More than 200 laboratory test results were available. This database is a potential data source for producing descriptive studies, comparative effectiveness studies, studies of adverse effects, and prediction studies. The Real World Database provides an opportunity and strategy to produce real-world evidence for Japan.

Per una terminologia condivisa dei dizionari elettronici/digitali

The contribution analyses the history of the phrases electronic dictionary and digital dictionary with the aim of identifying a shared specialised terminology. It also considers other naming proposals in their relation to the specific typologies, from the point of view of their genesis (first generation or primary if dictionaries are realised directly as computer tools, second generation or secondary if designed as paper-based tools and only subsequently computerised), their nature (first level if computerisation relates only to the text, second level if it also concerns the structure of the entry and thus transforms the dictionary into a real database), and the mode of consultation (introducing the concept of dynamic dictionary). Collaterally, the contribution outlines a history of computer lexicography, with more details on the first realisations.

Comparative Interrupted Time Series Analysis of Direct Medical Expense and Length of Stay in Elderly Patients with Femoral Neck Fractures Who Underwent Total Hip Arthroplasty and Hemiarthroplasty: A Real World Nationwide Database Study.

The objective of our study was to analyze the postoperative direct medical expenses and hospital lengths of stay (LOS) of elderly patients who had undergone either hemiarthroplasty (HA) or total hip arthroplasty (THA) for femoral neck fractures and to determine the indication of THA by comparing those variables between the 2 groups by time. In this comparative large-sample cohort study, we analyzed data from the 2011 to 2018 Korean National Health Insurance Review and Assessment Service database. The included patients were defined as elderly individuals aged 60 years or older who underwent HA or THA for a femoral neck fracture. A 1:1 risk-set matching was performed on the propensity score, using a nearest-neighbor matching algorithm with a maximum caliper of 0.01 of the hazard components. In comparative interrupted time series analysis, time series were constructed using the time unit of one-quarter before and after 3 years from time zero. For the segmented regression analysis, we utilized a generalized linear model with a gamma distribution and logarithmic link function. A total of 4,246 patients who received THA were matched and included with 4,246 control patients who underwent HA. Although there was no statistically significant difference in direct medical expense and hospital LOS for the first 6 months after surgery, direct medical expenses and hospital LOS in THA were relatively reduced compared to the HA up to 24 months after surgery (p < 0.05). In the subgroup analysis, the THA group's hospital LOS decreased significantly compared to that of the HA group during the 7 to 36 months postoperative period in the 65 ≤ age < 80 age group (p < 0.05). Direct medical expenses of the THA group significantly decreased compared to those of the HA group during the period from 7 to 24 months after surgery in the men group (p < 0.05). When performing THA in elderly patients with femoral neck fractures, the possibility of survival for at least 2 years should be considered from the perspective of medical expense and medical utilization. Additionally, in healthy and active male femoral neck fracture patients under the age of 80 years, THA may be more recommended than HA.

Intelligent Face Recognition Based Students’ Attendance System

This paper aims at developing an intelligent face recognition students’ attendance system to enhance school attendance tracking. The system is made up of four phases- database of students’ details, face detection, face recognition and attendance report. The database stores all the students’ details and images of the students’ face captured while face detection and recognition is carried out with convolutional neural network algorithms from the face recognition and opencv library. As faces are detected and recognized from live streaming video of the classroom, attendance are recorded into an excel file and then sent to a real time database. The system also contains a mobile interface through which the course instructors can access information at all time. The methodology adopted for this work is object-oriented analysis and design methodology (OOADM) while programming language used is Python. This work has helped immensely to eliminate the issue of proxy attendance as well as reduce the time wasted in manual attendance system. It is very beneficial to schools and other institutions where attendance is required.

Method for holistic optimization of the manufacturing process numerically described as low-dimensional database

The management of the production processes in an optimal manner involves the usage of knowledge about past jobs as reference for current decisions. During a manufacturing flow in every process step the process engineers could be in situations that request quick decisions based on comparison of different potential manufacturing paths. The Method for Holistic Optimization was developed in order to be used as support for decisions. The method was validated thru different studies. For the mentioned studies there were used artificial and real instances databases. The approach of the optimal management of the manufacturing processes was developed in the current study in order to estimate the consequences of a decision, are used known methods, such as: NN modeling, big data analysis, statistics, etc. In all these cases, the database size plays an essential role in terms of estimation quality. The main purpose of the study is to analyze and validate that the Method for Holistic Optimization is feasible to be used in case a decision-maker uses a reduced database. This can be a significant advantage compared with other methods. The study it is performed using an instance database which was artificially generated in case of a turning process. The obtained results are consistent and promising.

Adaptive chimp optimization algorithm based secure workload control strategy in real time database management systems

To control various workloads, a Real-Time Database Management System (RTDMS) serves as a framework for executing transactions. Database management systems support both the storage and recovery of data across various application services. However, since security and Quality of Service (QoS) are evaluated separately during user transactions, the performance of these transactions has not been optimized. To address these issues, this paper incorporates both security strength and QoS optimization. In this paper, workload conditions during user transactions in real-time database systems are managed using the Adaptive Chimp Optimization Algorithm (AChOA). This algorithm enhances the security strength of the RTDMS by optimally selecting the security policy based on user requests. Additionally, the search performance of the Chimp Optimization Algorithm (ChOA) is improved through the use of a chaotic series generator with tent mapping. Moreover, an Intrusion Detection and Protection System (IDPS) with a high detection rate is implemented to improve response time. Simulation results demonstrate that the proposed scheme achieves better security strength and response time.

Age and gender detection with real time database

Age and gender detection with real time database

Digital ecosystem of Russian Agricultural Bank as an innovative platform for sustainable development of the agro-industrial complex

The digital ecosystem for the agro-industrial complex emerged as a platform that unites agrarians from all regions of Russia and stimulates the promotion of agro-technologies in rural areas. The successes demonstrated by the agribusiness sector since 2017 need to be consolidated and developed, which is why a unique industry ecosystem “Your Farm” was built on the basis of the Russian Agricultural Bank (RusAg) in the conditions of digital interaction. The digital platform is a unique megaservice that unites farmers and allows them to solve questions on the sale and supply of agricultural products. It is also used to attract various types of state support, find or train qualified employees, develop agro-tourism and promote rural areas. It also allows farmers to form a real estate database. RusAg ecosystem continues to develop and expand its services, the number of users of the platform’s services is increasing, the popularity of rural lifestyle is growing. The “Your Farm” digital ecosystem has turned an industry bank into a technological platform. It promotes technical modernization of the agro-industrial complex, expands its export potential, attracts specialists to work in rural areas, and improves the quality of life in the regions.

Mining credible attribute rules in dynamic attributed graphs

Graph mining has numerous real-world applications. The goal is to extract interesting subgraphs or patterns in graph databases. Dynamic attributed graphs are more complex databases in which graphs change over time, and each vertex has multiple attributes. However, most algorithms only pay attention to finding rules that show relationships between nodes, and they typically have substantial limitations, such as finding rules with a single attribute or vertex and edges that do not change over time. To address these limitations, we propose an algorithm called CAR-Miner, which aims to mine credible attribute rules in dynamic attributed graphs. The algorithm incorporates a novel objective interestingness measure that is stable, anti-monotonic, and can eliminate cross-support core patterns. We conducted several experiments on real-life databases. The results show that our algorithm outperforms the state-of-the-art algorithm, especially as the number of attributes increases. Additionally, we find patterns that have practical significance and can be interpreted in various ways. Overall, CAR-Miner is a promising algorithm for mining patterns in dynamic attributed graphs, which can help researchers and practitioners identify valuable patterns that were previously difficult to discover.

Solving an Unrelated Parallel Machines Scheduling Problem with machine- and job-dependent setups and precedence constraints considering Support Machines

Single-stage production planning problems are common in the academic literature and in real-world environments. One of the least studied scenarios in the literature for this environment is that with Unrelated Parallel Machines. In this work, this type of problem is inspired by a real station within the wind tower production process. The problem presents characteristics that have already been studied, such as mandatory precedences and setup times that depend on the machine and the job. However, a new and real feature is presented: the existence of “support machines”, which are machines that can continue to work but cannot complete jobs due to reduced capacity for some operational reason. In this case, instead of abandoning their work, support machines can still be used to assist other machines by performing partial jobs before handing them over to full-capacity machines for completion. This innovative concept of support machines, never before presented in this context of production planning, introduces a unique approach to dealing with reduced-capacity machines without sacrificing their operational potential. A Mixed-Integer Linear Programming (MILP) model is formulated to mathematically represent this problem.This work explores the impact of these support machines on production planning. For this purpose, Tabu Search and Simulated Annealing metaheuristics have been adapted for their solution, and a novel Constructive Heuristic has been developed based on the real and manual process currently performed in the aforementioned factory. These three algorithms are run and compared on a real database in order to minimise the makespan. Their analysis shows that although the use of support machines generally gives positive results, an improvement is not achieved in all cases. Furthermore, contrary to what might be expected, the use of full-capacity machines in the role of support machines sometimes improves completion times.

Denoising Diffusion Probabilistic Models and Transfer Learning for citrus disease diagnosis.

Plant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations in the distribution of state samples, and the lack of sufficient real disease databases weakens the information carried by the samples, posing challenges for accurately training models. This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in diagnosing citrus diseases with a small sample. Two training methods are proposed: The Method 1 employs the DDPM to generate synthetic images for data augmentation. The Swin Transformer model is then used for pre-training on the synthetic dataset produced by DDPM, followed by fine-tuning on the original citrus leaf images for disease classification through transfer learning. The Method 2 utilizes the pre-trained Swin Transformer model on the ImageNet dataset and fine-tunes it on the augmented dataset composed of the original and DDPM synthetic images. The test results indicate that Method 1 achieved a validation accuracy of 96.3%, while Method 2 achieved a validation accuracy of 99.8%. Both methods effectively addressed the issue of model overfitting when dealing with a small dataset. Additionally, when compared with VGG16, EfficientNet, ShuffleNet, MobileNetV2, and DenseNet121 in citrus disease classification, the experimental results demonstrate the superiority of the proposed methods over existing approaches to a certain extent.

Multilevel-based algorithm for hyperspectral image interpretation

Hyperspectral imagery contains spatial and spectral information, which can reveal the material properties of the target while intuitively displaying its spatial attributes. It has been applied in target recognition, search and rescue, and other fields. However, manual detection inevitably leads to missed detections and false alarms, necessitating the assistance of artificial intelligence for detection. To address this, we propose the multilevel-based algorithm for hyperspectral image interpretation. 1) From the spatial and spectral dimensions, we propose a semantic segmentation algorithm based on multidimensional information fusion to achieve semantic segmentation. 2) From the semantic and textual representation dimensions, we introduce a context interpretation module based on visual attention. We construct both real and simulated databases to validate the effectiveness of the algorithm. Experimental results demonstrate that the average accuracy of semantic segmentation achieved by the proposed algorithm is 74.3%. Additionally, the BLEU1 score reaches 71.2, outperforming mainstream algorithms by 1.4.

On hierarchical clustering-based approach for RDDBS design

Distributed database system (DDBS) design is still an open challenge even after decades of research, especially in a dynamic network setting. Hence, to meet the demands of high-speed data gathering and for the management and preservation of huge systems, it is important to construct a distributed database for real-time data storage. Incidentally, some fragmentation schemes, such as horizontal, vertical, and hybrid, are widely used for DDBS design. At the same time, data allocation could not be done without first physically fragmenting the data because the fragmentation process is the foundation of the DDBS design. Extensive research have been conducted to develop effective solutions for DDBS design problems. But the great majority of them barely consider the RDDBS's initial design. Therefore, this work aims at proposing a clustering-based horizontal fragmentation and allocation technique to handle both the early and late stages of the DDBS design. To ensure that each operation flows into the next without any increase in complexity, fragmentation and allocation are done simultaneously. With this approach, the main goals are to minimize communication expenses, response time, and irrelevant data access. Most importantly, it has been observed that the proposed approach may effectively expand RDDBS performance by simultaneously fragmenting and assigning various relations. Through simulations and experiments on synthetic and real databases, we demonstrate the viability of our strategy and how it considerably lowers communication costs for typical access patterns at both the early and late stages of design.

Ensemble Learning for Multi-Label Classification with Unbalanced Classes: A Case Study of a Curing Oven in Glass Wool Production

The Industrial Internet of Things (IIoT), which integrates sensors into the manufacturing system, provides new paradigms and technologies to industry. The massive acquisition of data, in an industrial context, brings with it a number of challenges to guarantee its quality and reliability, and to ensure that the results of data analysis and modelling are accurate, reliable, and reflect the real phenomena being studied. Common problems encountered with real industrial databases are missing data, outliers, anomalies, unbalanced classes, and non-exhaustive historical data. Unlike papers present in the literature that respond to those problems in a dissociated way, the work performed in this article aims to address all these problems at once. A comprehensive framework for data flow encompassing data acquisition, preprocessing, and machine class classification is proposed. The challenges of missing data, outliers, and anomalies are addressed with critical and novel class outliers distinguished. The study also tackles unbalanced class classification and evaluates the impact of missing data on classification accuracy. Several machine learning models for the operating state classification are implemented. The study also compares the performance of the proposed framework with two existing methods: the Histogram Gradient Boosting Classifier and the Extreme Gradient Boosting classifier. It is shown that using “hard voting” ensemble learning methods to combine several classifiers makes the final classifier more robust to missing data. An application is carried out on data from a real industrial dataset. This research contributes to narrowing the theory–practice gap in leveraging IIoT technologies, offering practical insights into data analytics implementation in real industrial scenarios.

RoCNet++: Triangle-based descriptor for accurate and robust point cloud registration

This paper introduces RoCNet++, a point cloud registration method with two main contributions, one concerning the design of a robust descriptor and another concerning the estimation of the rigid transformation. First, to robustly capture the local geometric properties of the surface, i.e., each point is characterized by all the triangles formed by itself and its nearest neighbours in the 3D point cloud. The idea is to assist the learning of the descriptor by introducing a priori information about interesting geometric properties such as the invariance of triangle angles under rigid transformations. This local triangle-based descriptor is integrated into the recently developed RoCNet architecture for estimating the correspondences between source and target point clouds. We then introduce the Farthest Sampling-guided Registration (FSR), which relies on successive farthest point samplings to estimate the global rigid transformation between 3D point clouds. The new proposed architecture RoCNet++ has been evaluated in different configurations: clean, noisy and partial data on both synthetic and real databases such as ModelNet40, KITTI, and 3DMatch. RoCNet++ shows improved performances on these benchmark datasets in favourable and unfavourable conditions. Furthermore, both the local triangle-based descriptor and the Farthest Sampling-guided Registration (FSR) can be used in other registration algorithms.

Deep convolutional and conditional neural networks for large-scale genomic data generation.

Applications of generative models for genomic data have gained significant momentum in the past few years, with scopes ranging from data characterization to generation of genomic segments and functional sequences. In our previous study, we demonstrated that generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be used to create novel high-quality artificial genomes (AGs) which can preserve the complex characteristics of real genomes such as population structure, linkage disequilibrium and selection signals. However, a major drawback of these models is scalability, since the large feature space of genome-wide data increases computational complexity vastly. To address this issue, we implemented a novel convolutional Wasserstein GAN (WGAN) model along with a novel conditional RBM (CRBM) framework for generating AGs with high SNP number. These networks implicitly learn the varying landscape of haplotypic structure in order to capture complex correlation patterns along the genome and generate a wide diversity of plausible haplotypes. We performed comparative analyses to assess both the quality of these generated haplotypes and the amount of possible privacy leakage from the training data. As the importance of genetic privacy becomes more prevalent, the need for effective privacy protection measures for genomic data increases. We used generative neural networks to create large artificial genome segments which possess many characteristics of real genomes without substantial privacy leakage from the training dataset. In the near future, with further improvements in haplotype quality and privacy preservation, large-scale artificial genome databases can be assembled to provide easily accessible surrogates of real databases, allowing researchers to conduct studies with diverse genomic data within a safe ethical framework in terms of donor privacy.

EOG Artifacts Suppression From single channel EEG Signals by VME-GMETV model

Nowadays the usage of portable electroencephalography (EEG) recorded systems are raised for recording of neuro activities of the brain, which are useful in clinical diagnosis such as brain–computer interface (BCI) applications. The eye movements or blinks are unavoidable activities, which reflects the electrooculogram (EOG) artifacts in the recorded EEG signals, which leads deceptive interpretation of the underlying brain state. In this paper a novel framework is proposed as Variational Mode Extraction (VME) method is integrated with Generalized Moreau Envelope Total Variation (GMETV) technique to filter the EOG artifacts. Initially, the contaminated EEG is applied to the VME method which extracts the EOG artifact segment. Then, this EOG artifact segment is given to the GMETV technique and it separates the wanted component of the EEG residue from the EOG artifact segment. The EEG residue is added back to the non EOG artifact segment signal which gives the final denoised EEG. Matlab simulations are done on both simulated and real time EEG databases to evolute the proposed VME-GMETV technique performance in terms of Relative Root Mean Square Error (RRMSE), Correlation Coefficient (CC) and Mean Absolute Error (MAE). From the experimental results, the proposed technique is outperformed the existing methods by averaged low RRMSE (0.1557), MAE (5.0714e−04) and averaged high CC (0.9695).

Proposed Method for Estimating Parameters of Archimedean Spirals

AbstractConsider an Archimedean spiral in two-dimensional space series of points subject to random errors. In this paper, a methodology of a fitting procedure to spiral curve is studied. Three methods are proposed to give initial estimates of the spiral parameters. An Optimize algorithm proposed to updates the initial estimates. The approach is validated using simulated and real databases obtained from Parkinson patients handwriting. Finally, a comparison done between these three methods and Mishara’s method which shown that our methods give better results.

Identification of the model parameters in cornea with keratoconus in terms of the numerical-experimental study of corneal geometry and mechanical behavior

The aim of this work is to derive a computational technique for model parameter es-timation of corneal mechanical behavior basing upon the following input clinical bi-omarkers: the real-time amplitude of corneal deformations, measured in course of non-contact pneumotonometry, intraocular pressure and geometric features of the patient’s cornea, revealed during his tomographic examination. The developed technique of corneal properties prediction within the area of kerato-conus and beyond it, is based upon the data resulting from finite element modeling of a non-contact tonometric test, and upon the procedure of multi-parameter optimization (minimization) of the deviation of the calculated results from the amplitude of corneal deformation measured by means of pneumotonometer Corvis ST. The simulation is based on the results of a survey of 78 patients of a real clinical database, taking into account the geometry of the cornea, the size and position of the keratoconus zone, the intraocular pressure, and the deformation parameters of the material during the pneumotest, which are specific for each patient.

데이터베이스 이용제한 행위에 대한 경쟁법 적용

In a data-driven economy, companies (esp. platform companies) with large amounts of data can use it to gain competitive advantages, such as product and service innovation and improvements. However, when big data is concentrated in the hands of a few companies, new entrants may find it difficult to enter the market or, if they do, to compete. It is legal for companies to collect significant amounts of data and to manage and control it. However, the issue of misuse of data to increase entry costs and extend market power is being debated in various countries around the world on the need for regulation at the competition law. This study will introduce the Canadian Competition Bureau's Intellectual Property Enforcement Guidelines (the “IP Guidelines”) and the case of the Toronto Real Estate Board's (the “TREB”) restrictions on the use of a real estate database in the Toronto area, which addressed whether the restrictions constituted an abuse of market power under Canadian competition law(the “Case”).&#x0D; The case has been a long-running legal battle that began in 2011 with an investigation by the Canadian Competition Bureau and concluded in 2018 with a decision by the Supreme Court of Canada. As such, it is considered to be a precedent-setting case in the context of determining abuses of market power in relation to database restrictions. Recently, the Korea Fair Trade Commission also imposed a corrective order and a fine on NAVER for the act of preventing a third party (a competitor such as Kakao) from providing real estate listing information provided by NAVER through a contract with a real estate information provider. The case contains many similar legal issues to the NAVER case and is worthy of comparison and analysis. This study draws implications for Korean competition law enforcement through foreign regulations and case studies on platform operators' data access restriction.