Data Storage System Research Articles

Comparison of methods for optimizing the speed of reading/writing drives

The objects of this study are data storage devices of various types and levels of complexity, as well as the principles of their operation. They are complex technical systems that include many components and are characterized by a high degree of integration. The subject of the research is to study the main characteristics of hard drives and solid-state drives. Their structure, functional features, principles of operation and ways of optimization are important. The purpose of the study is to determine the most effective methods for optimizing the operation of these devices. This includes aspects such as memory management, load balancing, power management, and others. The results of this research can be used to improve data efficiency, improve the performance of data storage systems and create new technologies in this area. This study examines the performance of various disk storage solutions through a series of tests aimed at understanding speed and dependence on external factors. The main conclusions of the study reflect the importance of the integrated use of optimization approaches to improve the speed of reading and writing data. Optimizing the processes of reading and writing data is critically important for modern high-performance computing systems, as well as for applications that require quick access to large amounts of information. The improved techniques used in the course of the study contribute to a significant increase in the performance of data storage devices. They take into account the specifics of various types of storage devices, including hard drives and solid-state drives, and offer optimization approaches that take into account their unique characteristics. Overall, the results of this study provide valuable insights into the principles of optimizing data storage, and they can serve as a basis for developing new strategies and solutions in this important area of information technology. This study represents a significant contribution to the scientific understanding of optimizing data reading and writing processes, and its findings may have long-term implications for the development of data storage technologies.

Comprehensive evaluation and enhancement of Reed-Solomon codes in RAID6 data storage systems

This paper provides an in-depth examination and optimization of Reed-Solomon codes within the context of Redundant Array of Independent Disks 6 (RAID6) data storage configurations. With the swift advancement of digital technology, the need for secure and efficient data storage methods has sharply escalated. This study delves into the application of Reed-Solomon codes, which are acclaimed for their unparalleled ability to rectify multiple errors, and their crucial role in maintaining RAID6 system operation even under multiple disk failures. The intricacies of Reed-Solomon codes are scrutinized, and the system's resilience in various disk failure scenarios is evaluated, contrasting the performance of Reed-Solomon codes with other error correction methodologies like Hamming codes, Bose-Chaudhuri-Hocquenghem codes, and Low-Density Parity-Check codes. Rigorous testing underscores the robust error correction capabilities of Reed-Solomon encoding in an array of scenarios, affirming its efficacy. Additionally, potential enhancement strategies for the implementation of these codes are proposed, encompassing refinements to the algorithm, the adoption of efficient data structures, the utilization of parallel computing techniques, and hardware acceleration approaches. The findings underscore the balance that Reed-Solomon codes strike between robust error correction and manageable computational complexity, positioning them as the optimal selection for RAID6 systems.

Using Technology and Algorithms for Face Detection and Recognition Using Digital Image Processing and Relying on a Computer Vision Sensor

Advancing in variable scopes in network technology, many new technologies were developed. Security issues were important, especially in the detection and recognition of people using variable methods like face details. Sensors have been used widely in recent days to support security systems. Sensors are devices used to convert any type of signals into electrical signals that are recorded to be processed later. These signals can be viewed by the user in several ways. Sensors increased in the development stage that can be integrated with operating systems, data storage systems, processing units, communication units, and any other function units. Detection and recognition systems were developed into a new level of technology. Some systems like figure print and palm lines face many problems because the possible change of the skin structure can be faced in time. So, these methods faced a certain problem and limitations that caused them to search for other methods more accurate. This search aims to create a new method for face detection and recognition depending on sensors. Most of the methods used for face recognition depend on OpenCV libraries that give good accuracy and time recovery availability. On the other hand, practical applications were developed to increase the accuracy of these systems like SeetaFace and YouTu methods. Three methods of detection were important to be detected too to increase the accuracy of the whole system which are the side face detection, the occlusion detection, and the face expressions. Then these data were compared to create the whole accuracy result of the system.

Development of an IoT-Based Automatic Remote Health Monitoring System

A well-planned health monitoring system is essential for the modern health center, and a portable remote health monitoring system with many intellectual features is becoming a great improvement in healthcare arrangements. The proposed system includes various micro-electronics medical devices and applications that come through network-connected devices and help to monitor patients' real-time medical data. In this paper, the patient's health condition has been monitored by using the four types of major health parameters temperature sensor, heart pulse rate sensor, blood pressure sensor module, and blood oxygen (SpO2) sensor. All the sensor nodes are connected to the Raspberry Pi-based embedded system. The real-time health data are recorded, stored, and transmitted to the doctor via the internet with the help of a Raspberry Pi-based embedded system and IoT server. The system will benefit the patient from remote areas with quick diagnosis, remote observation, home observation, and a medical data storage system. Journal of Engineering Science 14(2), 2023, 21-30

Secure cloud storage for IoT based distributed healthcare environment using blockchain orchestrated and deep learning model

Internet of Things (IoT), a distributed healthcare system has integrated different medical resources with sensors and actuators. In this research paper proposes a secure healthcare monitoring system for IoT based distributed healthcare systems in the cloud using blockchain and deep learning (DL) mechanisms. The proposed system involved three phases: secure data transmission, data storage, and disease classification system. Initially, the patients are authenticated via blockchain mechanism and their data is encrypted via Effective Key-based Rivest Shamir Adelman (EKRSA), in which the keys are generated using Circle chaotic map and Linear inertia weight-based Honey Badger Optimization (CLHBO) algorithm. Next, in the data storage phase, these encrypted IoT data are securely stored in the cloud using blockchain technology in a distributed manner. Finally, in the disease classification, the data are gathered from the publicly available dataset, and these collected datasets are preprocessed to handle missing values and data normalization. After that, the proposed system applies a radial basis kernel-based linear discriminant analysis (RBKLDA) model to reduce the dimensionality of the dataset. At last, the disease classification is done by optimal parameter-centered bidirectional long short-term memory (OPCBLSTM). The proposed EKRSA system archives maximum throughput of 99.05% and reliability of 99.66, which is superior to the existing approaches. The OPCBLSTM is investigated for its disease classification process, the proposed one achieves 99.64% accuracy with less processing time of 6 ms, which is superior to the existing classifiers. The experimental analysis proves that the system attained better security and classification metrics results than the existing methods.

Parameters of some BCH codes over $ \mathbb{F}_q $ of length $ \frac{q^m-1}{2} $

BCH codes, known for their error-correcting capability and efficient encoding and decoding algorithms, are an important class of cyclic codes over finite fields. Currently, BCH codes have been widely applied in consumer devices, communication systems, and data storage systems. However, there is still a lot of unsolved work in exploring the parameters of BCH codes, such as the dimensions and minimum distances. The objective of this paper is to study narrow-sense BCH codes of length $ \frac{q^m-1}{2} $ over $ \mathbb{F}_q $. The parameters of narrow-sense BCH codes of such length with designed distances $ \delta_i = \frac{q^m-q^{m-1}-q^{\lfloor\frac{m-3}2\rfloor+i}-1}{2} $ and $ \delta_{\lfloor\frac{m+6}{4}\rfloor+t} = \frac{q^m-q^{m-1}-q^\frac{3m-1}{4}-q^\frac{m+1}{2}-q^{\frac{m-11}{4}+t}-1}{2} $ are presented, respectively, where $ 1\le i\le \lfloor\frac{m+6}{4}\rfloor $ and $ 2\le t\le 4 $.

Iraqi Population Trusts in Electronic Healthcare Records: A Cross-sectional Study.

This study investigated the perceptions of Iraqi patients regarding Electronic Healthcare Records (EHRs) in terms of trust and propensity to share and exchange medical and personal information and data within the healthcare ecosystem. During the period of April to June 2022, a researcher-assisted questionnaire was disseminated to adult Iraqi patients attending public or private healthcare facilities in a subset of Iraqi governorates. Data collection was followed by descriptive and inferential analyses. In total, 552 respondents filled out the questionnaire. The findings revealed that 71.6% of respondents were conversant with EHRs and trusted them as data collection and storage systems. In addition, 10% of respondents did not want their EHRs to be shared between healthcare professionals and institutions. However, only 3.6% of participants were willing to share all of their personal information with healthcare professionals. Female respondents were considerably more willing to share their full names with healthcare professionals than male respondents, despite the society's reputation for conservatism. The findings of this study highlighted the necessity of tailoring initiatives to enhance patients' trust in EHRs and their interactions with healthcare professionals other than medical physicians.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

With the development of applications related to image processing technology, the target requirements of data acquisition and analysis systems are constantly being optimized. Research on data acquisition and analysis based on the integrated SIFT (Scale Invariant Feature Transform) algorithm is becoming increasingly important. The construction of a data collection and analysis system focuses on ensuring efficient and accurate data collection and analysis to provide valuable insights and decision support. Key priorities include determining data requirements and objectives, designing appropriate data collection methods and tools, establishing reliable data storage and management systems, developing effective data analysis algorithms and models, and ensuring data security and privacy protection. This article analyzed the theoretical analysis of feature point extraction algorithm and the process steps and application of integrated SIFT algorithm. With the help of dimensionality reduction processing, the SIFT algorithm was improved and optimized. Combined with the scale space calculation formula, The conclusion is as follows: On the basis of image processing, the data collection and analysis system using the improved integrated SIFT algorithm on the five selected image file samples has improved the accuracy of feature point matching, with a comprehensive average improvement of 7.6%. The efficiency of feature point extraction has also been improved, with a comprehensive average improvement of about 8%. This indicated that the data collection and analysis system based on the improved integrated SIFT algorithm has good practical application results in feature point matching and extraction.

Doenças autoinflamatórias: um estudo multicêntrico latino-americano de acordo com o sexo e a idade

ABSTRACT Objective: To evaluate autoinflammatory diseases (AID) according to age at diagnosis and sex, and response to therapy in a large population. Methods: This is a cross-sectional observational study of a Latin American registry using a designed web system for data storage, collected between 2015 and 2018. Any altered findings during follow-up were recorded. The forms were translated into Portuguese and Spanish, including demographic, clinical, laboratory, genetic and treatment characteristics. Results: We included 152 patients, 51.3% male and 75% Caucasian. The median age at disease onset was 2.1 years (0–15.6 years) and median age at diagnosis 6.9 years (0–21.9 years); 111 (73%) were children (0–9 years old), and 41 (27%) were adolescents and young adults (AYA) (10–21 years old). Periodic fever, aphthous stomatitis, pharyngitis, and adenitis syndrome (PFAPA) occurred in 46/152 (30%), chronic non-bacterial osteomyelitis (CNO) in 32/152 (21%), and familial Mediterranean fever (FMF) in 24/152 (15.7%). PFAPA was significantly higher in young children than in AYA (38.7% vs. 7.3%, p<0.001), while CNO were lower (13.5% vs. 41.5%, p<0.001). The frequency of females was significantly higher in CNO (28.4% vs. 14.1%, p=0.031) and lower in FMF (8.1% vs. 23.1%, p=0.011). The most used drugs were glucocorticoids, non-steroidal anti-inflammatory drugs (NSAID), and colchicine. Glucocorticoids and colchicine treatment were used in all AID with good to moderate response. However, cryopyrin-associated periodic syndromes (CAPS) seemed unresponsive to glucocorticoids. NSAIDs and methotrexate were the main medications used to treat CNO. Conclusions: Differences among AID patients were observed in the LA population regarding sex and age at disease diagnosis.

Digital Consciousness and Identity

The aim of the study is to analyze how the development of digital consciousness and the widespread ingress of digital technologies into daily life affect the under-standing and formation of human identity in contemporary society. This aim encompasses several key research directions: studying the impact of virtual agents and artificial intelligence systems with elements of digital conscious-ness on individuals’ self- perception and their views of the world; identifying the role of digital media in the process of forming and rethinking social identity; analyzing the consequences of continuous interaction with digital technologies and content for personal development, self-esteem, and self-presentation; considering ethical and philosophical issues related to the creation and interaction with digital conscious-ness; reflecting on potential pathways for the development of relationships between humans and digital technologies, assessing opportunities and risks associated with a closer integration of artificial intelligence into the social and personal spheres of people’s lives. The result of exercising this interdisciplinary approach and combining various theories and observations show that people, with the help of technology, are taking part in developing their own identity, further proving the impact of digitalization on an individualistic and collective level; digital technologies also tend to have a profound and transformative impact on the peoples’ self-awareness and relations in a globalized world; furthermore, the use of digital technologies proves to be an effective means of global and subtle influence on the life, behavior and beliefs of modern people. It has been argued that postmodernism and cyberculture, in addition to having an ontological connection, share a model of world perception, which is not affiliated with any type of hierarchical model. The result of the author's reasoning is the conclusion that changes in technology or data loss are not mere technical problems – people end up feeling serious consequences of them, including drastic shifts in their personality, cognitive abilities and general well-being. This deeply affects the user’s ability to maintain their productivity, interact with information, and, more broadly, their ability to function in everyday life. Such situations highlight the need for the development and implementation of strategies and policies that consider potential vulnerabilities associated with technology dependence and aim to minimize the negative consequences of such dependence. Ethical considerations around cognitive extenders should include discussions on precautionary measures and development standards that ensure protection and support for users in the event of technological changes, updates, or failures. This might involve creating more resilient systems for data storage and recovery, developing more transparent and predictable update schemes, and introducing policies that support users’ cognitive autonomy and well-being.

DETECTION, ANALYSIS AND PROTECTION OF CONFIDENTIAL DATA USING AMAZON MACIE MACHINE LEARNING TECHNOLOGY

Over the past decades, the field of data storage and processing has undergone significant changes and expansion, especially with the advent of cloud technologies and computing. Cloud services enable organizations to store and access large amounts of data through distributed systems. However, along with these new opportunities come new challenges, particularly in the area of protecting confidential data. Protecting sensitive data is an extremely important task for today's organizations, especially in the face of a growing number of digital threats and security breaches. In order to ensure reliable protection of valuable and sensitive information, developers and researchers are actively working on the development of new technologies and tools. One of the powerful tools used to identify, analyze and protect confidential data is the machine learning technology of the Amazon Macie service. Amazon Macie is an AWS cloud computing service that uses artificial intelligence and machine learning algorithms to automate data analysis and identify potential data security threats. The main purpose of this work is the detection, analysis and protection of confidential data using Amazon Macie machine learning technology. Amazon Macie is an innovative service developed by Amazon Web Services (AWS) that uses advanced machine learning algorithms for automated discovery and analysis of sensitive data. As part of the work, an analysis of the main machine learning algorithms, principles of data storage systems and methods of protecting confidential information was carried out. The working principles and capabilities of Amazon Macie, which uses advanced machine learning algorithms for automated data analysis and detection of potential threats to data security, were investigated.

Data Management across Geographically-Distributed Autonomous Systems: Architecture, Implementation, and Performance Evaluation

Supporting efficient geographically-distributed autonomous data management is one of the critical obstacles for opening up a big data era. It creates the need for investigating such kind of systems in big data environments. In this paper, a distributed autonomous data management system is put forward, exhibiting the following features. (1) A distributed architecture designed to meet the requirements of autonomous data management by allowing interconnection, intercommunication, and interoperation of multiple sites over the Internet. (2) An autonomous, multi-level, unstructured data storage system to meet high-efficiency storage needs, with reference to the distributed heterogeneous data storage theories. (3) A distributed autonomous data indexing and retrieval system to support metadata search & full-text searching, fast loading, remote access, and unified view. Experimental and industrial application results demonstrate that the proposed system has high potential to reduce access time and improve storage efficiency, while maintaining satisfactory availability and scalability.

Tools for ranking objects of the mineral resource complex based on ontology

Mineral resources form the basis for the development of the vast majority of industries. At the same time, most of them are non-renewable. To determine promising exploration activities in conditions of limited human, financial, and sometimes technical resources, it is necessary to take into account a large number of factors when setting exploration tasks for the reproduction of mineral resources. To solve problems with relatively weak formalization, it is customary to use the mathematical apparatus of fuzzy logic in decision support systems. However, it must be based on an ontological model that will contain the interrelationships of the elements of the system and allow you to find implicit relationships between them. In this paper, based on the mathematical apparatus of fuzzy logic and the Mamdani method, it is proposed to form an ontology of geological and economic monitoring in relation to a mineral resource facility (MRF), an industrial raw materials hub (IRMH) and an administrative entity. As a result, using the Reasoner component built into Protege, indicators of geological and economic monitoring, taxonomic units and the frequency of information collection for designing a decision support system in the geological industry were determined. It is possible to create such a convergence, taking into account the heterogeneity of data storage systems, on the basis of an ontological model. The advantage of using ontology is the high level of this tool flexibility, taking the form of heterogeneous data integration within a single storage system. This article attempts to use ontological models framework together with artificial intelligence for the geological subject area in terms of solid minerals, particularly common ones, and ground waters.

Вплив технології блокчейн на фінансовий сектор глобальної економіки

The aim of the article is to highlight the importance and relevance of blockchain technology for the financial sector of the global economy, as well as to find out how blockchain technology affects security and reliability in the financial sector, simplifying the processes of exchanging assets and making transactions; emphasizing the importance of blockchain technology in improving the settlement system, reducing time, and increasing market liquidity. With rapid technological development, the role of blockchain technology is growing as a key factor determining new directions for the financial sector. Since its emergence as the foundation for cryptocurrencies, blockchain has quickly evolved from an alternative concept to a powerful tool that has the potential to change the paradigm of how financial services work. Outdated methods of providing financial services have long been in need of revision. Challenges such as data entry into various systems and manual processing of information are becoming increasingly common in the financial sector. The centralized model is becoming increasingly vulnerable to cyberattacks, as all data is concentrated in one place. Consequently, to date, outdated methods of information systems and the workload of paper documents in the financial sector have proven to be not only ineffective, but also vulnerable to security risks. These problems arise from deficiencies in centralized systems that store and process sensitive information. Lack of transparency, the possibility of errors, and suspicion about security are obstacles to the effective functioning of financial institutions. To solve these problems and improve processes, it is necessary to move towards digital innovations that promote collaboration, increase trust, independence and ensure a high level of security. One of the most promising technologies that meets these requirements is blockchain, which is based on a distributed data storage system and cryptographic principles, which can provide a high level of security, transparency, and authenticity of information. It avoids intermediaries by allowing direct collaboration between participants, and it also makes it possible to store and transmit data in a secure environment without the risk of their modification.

Optimizing skyrmionium movement and stability via stray magnetic fields in trilayer nanowire constructs.

Skyrmioniums, known for their unique transport and regulatory properties, are emerging as potential cornerstones for future data storage systems. However, the stability of skyrmionium movement faces considerable challenges due to the skyrmion Hall effect, which is induced by deformation. In response, our research introduces an innovative solution: we utilized micro-magnetic simulations to create a sandwiched trilayer nanowire structure augmented with a stray magnetic field. This combination effectively guides the skyrmionium within the ferromagnetic (FM) layer. Our empirical investigations reveal that the use of a stray magnetic field not only reduces the size of the skyrmionium but also amplifies its stability. This dual-effect proficiently mitigates the deformation of skyrmionium movement and boosts their thermal stability. We find these positive outcomes are most pronounced at a particular intensity of the stray magnetic field. Importantly, the required stray magnetic field can be generated using a heavy metal (HM1) layer of suitable thickness, rendering the practical application of this approach plausible in real-world experiments. Additionally, we analyze the functioning mechanism based on the Landau-Lifshitz-Gilbert (LLG) equation and energy variation. We also develop a deep spiking neural network (DSNN), which achieves a remarkable recognition accuracy of 97%. This achievement is realized through supervised learning via the spike timing dependent plasticity rule (STDP), considering the nanostructure as an artificial synapse device that corresponds to the electrical properties of the nanostructure. In conclusion, our study provides invaluable insights for the design of innovative information storage devices utilizing skyrmionium technology. By tackling the issues presented by the skyrmion Hall effect, we outline a feasible route for the practical application of this advanced technology. Our research, therefore, serves as a robust platform for continued investigations in this field.

Modeling the Reliability of Distributed Data Storage Systems

Modeling the Reliability of Distributed Data Storage Systems

Analytical Review of Data Lakes and Perspectives of Application in the Field of Education

An analytical review of the development of Data Lakes and its application in various industries, as part of Big data concept solutions, was conducted. The available standard architectural solutions for the Data Lake organization are considered. Also, specialized areas that require different or additional aspects to solve the tasks, depending on the field of Data Lake use, are taken into account. For the proper organization of Data Lake, various data processing tools are used, including distributed data storage systems, semantic networks, and especially metadata. Metadata plays a huge role in recognizing the purpose of data and possible relationships between it and entities. An overview of the prospects for the use of Data Lake, in particular as context of Smart City, distance education and the education industry in general, was conducted.

INNOVATION IN DATA STORAGE TECHNOLOGIES: FROM CLOUD COMPUTING TO EDGE COMPUTING

In an era where data is the new gold, understanding the evolution and future trajectory of data storage technologies is crucial. This paper delves into the transformative journey from traditional storage methods to contemporary paradigms like cloud and edge computing, underpinned by the burgeoning influence of Big Data, IoT, AI, and machine learning. The study's aim is to provide a comprehensive analysis of these technologies, assessing their development, efficacy, and the challenges they face in meeting the escalating demands of data storage.
 The methodology employed is a meticulous synthesis of literature reviews, case studies, and comparative analyses. This approach facilitates an in-depth exploration of the historical evolution of data storage, the paradigm shifts from cloud to edge computing, and the interplay between technological advancements and user demands. The study also scrutinizes the security concerns inherent in these technologies and identifies strategic directions for future research. Key findings reveal that while cloud computing has revolutionized data storage with its scalability and flexibility, edge computing emerges as a vital solution to latency and bandwidth limitations. The integration of AI and machine learning is identified as a pivotal factor in enhancing the efficiency and intelligence of data storage systems. However, this integration presents unique challenges, necessitating innovative solutions. Conclusively, the study recommends a continued focus on innovation in data storage technologies, emphasizing the development of integrated, secure, and efficient solutions. Future research should particularly explore the potential of AI and machine learning in overcoming current limitations.
 The paper's scope encompasses a comprehensive overview of the current state and future potential of data storage technologies, making it a valuable resource for researchers, technologists, and policymakers in the field.
 Keywords: Data Storage Technologies, Cloud Computing, Edge Computing, Big Data, Internet of Things (IoT), Artificial Intelligence (AI).

A Novel System for Confidential Medical Data Storage Using Chaskey Encryption and Blockchain Technology

Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and provide an analysis of its security and performance characteristics. The resulting images were tested against a number of important metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), bit error rate (BER), Signal-to-Noise Ratio (SNR), Normalization Correlation (NC) and Structural Similarity Index (SSIM). Our results showing that the system provides a highly secure and scalable solution for storing confidential medical data, with potential applications in a wide range of healthcare settings.

Rapid Information Retrieval from DNA Storage with Microfluidic Very Large-Scale Integration Platform.

Due to its high information density, DNA is very attractive as a data storage system. However, a major obstacle is the high cost and long turnaround time for retrieving DNA data with next-generation sequencing. Herein, the use of a microfluidic very large-scale integration (mVLSI) platform is described to perform highly parallel and rapid readout of data stored in DNA. Additionally, it is demonstrated that multi-state data encoded in DNA can be deciphered with on-chip melt-curve analysis, thereby further increasing the data content that can be analyzed. The pairing of mVLSI network architecture with exquisitely specific DNA recognition gives rise to a scalable platform for rapid DNA data reading.