Data Storage Method Research Articles

Exploring data storage innovations: From DNA to holography

This paper delves into the evolution of data storage technologies, focusing on the solid-state drive (SSD) and hard disk drive (HDD) and their working principles. It explores the advancements in DNA as a storage medium, highlighting its exceptional data density and potential as a long-term storage solution. The paper discusses the challenges and limitations of DNA data storage, emphasizing the need for further research to overcome these hurdles. Additionally, the paper introduces holographic data storage as a novel optical storage technology and discusses its potential applications. It also touches on the decline of the optical disc industry due to the emergence of alternative storage technologies. The use of metal nanoparticles (MNPs) in optical disks is explored, emphasizing their role in high-contrast imaging and data storage. The paper concludes by highlighting the diverse methods of data storage, each with its unique advantages and drawbacks, and anticipates future improvements in electronic information processing technology, leading to faster data transmission and increased data storage capacity within the same volume of storage media.

MFEMDroid: A Novel Malware Detection Framework Using Combined Multitype Features and Ensemble Modeling

The continuous malicious attacks on Internet of Things devices pose a potential threat to the economic and private information security of end-users, especially on the dominant Android devices. Combining static analysis methods with deep Learning is a promising approach to defend against that. This kind of method has two limitations: the first is that the current single-permission mechanism is not insufficient to regulate interapplication resource acquisition; another problem is that current work on feature learning is dedicated to modifying a single network structure, which may result in a suboptimal solution. In this study, to solve the abovementioned problems, we propose a novel malware detection framework MFEMDroid, which combines multitype features analysis and ensemble modeling. The Provider feature, facilitating information requests between applications (apps) and serving as an indispensable data storage method, plays a vital role in characterizing app behavior. Hence, we extract permissions and Provider features to comprehensively characterize app behavior and probe potentially dangerous combinations between or within these features. To address oversparse datasets and reduce feature learning overhead, we employ an auto-encoder for feature dimensionality reduction. Furthermore, we design an ensemble network based on SENet, ResNet, and the evolutionary convolutional neural network Squeeze Excitation Residual Network (SEResNet) to explore the hidden associations between different types of features from multiple perspectives. We performed extensive experiments to evaluate its method performance on real-world samples. The evaluation results demonstrate that the proposed framework can detect malware with an accuracy of 95.38%, which is much better than state-of-the-art solutions. These promising experimental results show that MFEMDroid is an effective approach to detect Android malware.

Sub-Diffraction Readout Method of High-Capacity Optical Data Storage Based on Polarization Modulation.

The big data era demands an efficient and permanent data storage technology with the capacity of PB to EB scale. Optical data storage (ODS) offers a good candidate for long-lifetime storage, as the developing far-field super-resolution nanoscale writing technology improves its capacity to the PB scale. However, methods to efficiently read out this intensive ODS data are still lacking. In this paper, we demonstrate a sub-diffraction readout method based on polarization modulation, which experimentally achieves the sub-diffraction readout on Disperse Red 13 thin film with a resolution of 500 nm, exceeding the diffraction limit by 1.2 times (NA = 0.5). Differing from conventional binary encoding, we propose a specific polarization encoding method that enhances the capacity of ODS by 1.5 times. In the simulation, our method provides an optical data storage readout resolution of 150 nm, potentially to 70 nm, equivalent to 1.1 PB in a DVD-sized disk. This sub-diffraction readout method has great potential as a powerful readout tool for next-generation optical data storage.

Sensing Data Concealment in NFTs: A Steganographic Model for Confidential Cross-Border Information Exchange.

In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring data confidentiality, integrity, and vulnerabilities to evolving cyber threats. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange. However, the persistent challenge lies in protecting sensitive information amidst evolving digital landscapes. Among the burgeoning applications of blockchain technology, non-fungible tokens (NFTs) have emerged as digital certificates of ownership, securely recording various types of data on a distributed ledger. Unlike traditional data storage methods, NFTs offer several advantages for secure information exchange. Firstly, their tamperproof nature guarantees the authenticity and integrity of the data. Secondly, NFTs can hold both immutable and mutable data within the same token, simplifying management and access control. Moving beyond their conventional association with art and collectibles, this paper presents a novel approach that utilizes NFTs as dynamic carriers for sensitive information. Our solution leverages the immutable NFT data to serve as a secure data pointer, while the mutable NFT data holds sensitive information protected by steganography. Steganography embeds the data within the NFT, making them invisible to unauthorized eyes, while facilitating portability. This dual approach ensures both data integrity and authorized access, even in the face of evolving digital threats. A performance analysis confirms the approach's effectiveness, demonstrating its reliability, robustness, and resilience against attacks on hidden data. This paves the way for secure data transmission across diverse industries.

Low-cost IoT-enabled indoor air quality monitoring systems: A systematic review

Indoor air quality (IAQ) is a critical challenge much less controlled in comparison with outdoor air quality. Bad IAQ is related to significant health complications such as respiratory problems, heart disease, and cancer. Many people spend most of their days inside buildings and don’t have air quality monitoring systems. Therefore, the occupants don’t know when the space has a higher quantity of pollutants than recommended, saturating the environment, and compromising people’s health. This is a problem that can be addressed by using Internet of Things (IoT) technologies to develop monitoring systems that allow a greater number of possibilities regarding the storage and processing of data and access to information by the end user, assisting the decision-making process regarding the indoor air pollution problem. Real-time data can be compared to default values, alerting the user of that situation, and suggesting an action to decrease the air pollutants concentration. There already are multiple solutions involving IoT-based technologies, many of them using low-cost sensors. Those are analyzed in this systematic review. Furthermore, the COVID-19 pandemic pointed out the importance of IAQ monitoring to evaluate the risk of contamination. The microcontrollers, IAQ parameters, sensors, data storage and visualization methods used in monitoring systems have been analyzed. The results show that most of the studies store data in Cloud systems and use Web platforms for data consulting. However, sensor calibration and efficient energy consumption are challenges that still exist.

Inkjet-printed quantum dots on paper as concept towards high-density long-term data storage

Handling and storing the immense amounts of data native to the information age is a major challenge in terms of technological sustainability and energy demand. To date, tape storage remains the most widespread method for data archiving, while DNA data storage appears to offer the best data density and long-term stability in the future. However, DNA data storage is still in its infancy primarily due to economic and accessibility challenges. This emphasizes the need for more practical and readily available alternatives. We present a method for data storage utilizing inkjet printable quantum dots on paper with photoluminescence (PL) readout. Our proof of principle study showcases the ability to print and stack multiple bits of data on a single spot by exploiting the unique PL properties of quantum dots. This approach utilizes easily accessible resources, including a consumer-grade printer and paper as the substrate. Additionally, we perform initial stability tests, investigate scalability by controlling emission intensity, and evaluate the potential data density achievable by our approach.

Deep learning-based glioma grading and feature visualization analysis

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Research on the fast geolocation algorithm based on Bloom filter

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Microservice information and management system for digitalization of document flow of APCS projects

A microservice information management system (IMS) that allows automation the preparing process of the required documentation for the developed projects of the automated control systems for technological processes (APCS) is proposed. The main requirements for digitalization of document flow of such projects are outlined. The functionality of all services implemented in the IMS is considered. A data storage method is determined that consider the specifics of storing not only structured information (for example, about specific equipment models, specialists, etc.), but also information about special JSON models used in the process of the service operation or calculating the configuration of an automated process control system project. The principle of forming documents on automated process control systems projects in the proposed IMS is shown. The work is aimed at increasing the efficiency of the development and deployment of automated process control systems projects for companies offering comprehensive solutions for technological processes automation at industrial enterprises. Keywords automatic control system for technological processes (APCS), information management system, microservice architecture, digitalization, document flow

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.

Custom ASIC Design for SHA-256 Using Open-Source Tools

The growth of digital communications has driven the development of numerous cryptographic methods for secure data transfer and storage. The SHA-256 algorithm is a cryptographic hash function widely used for validating data authenticity, identity, and integrity. The inherent SHA-256 computational overhead has motivated the search for more efficient hardware solutions, such as application-specific integrated circuits (ASICs). This work presents a custom ASIC hardware accelerator for the SHA-256 algorithm entirely created using open-source electronic design automation tools. The integrated circuit was synthesized using SkyWater SKY130 130 nm process technology through the OpenLANE automated workflow. The proposed final design is compatible with 32-bit microcontrollers, has a total area of 104,585 µm2, and operates at a maximum clock frequency of 97.9 MHz. Several optimization configurations were tested and analyzed during the synthesis phase to enhance the performance of the final design.

THE SOUND ARCHIVE OF THE INSTITUTE OF ETHNOMUSICOLOGY OF THE RESEARCH SCIENTIFIC CENTER OF THE SLOVENIAN ACADEMY OF SCIENCE AND ARTS

Since the end of February 2022, Ukrainian science experienced significant disruption caused by the full-scale war launched by the Russian Federation against Ukraine. As has often been the case in the history of armed conflicts, they have significantly affected various areas of culture, art and science, including ethnomusicology and its subject of research. The exodus of scholars within the country and abroad, the relocation and risk of destruction of archives, the more difficult condition for work in the field due to the martial law, the reduction of state funding in the fields of science and education, and, at the same time, the growing demand of society for traditional culture and the emergence of new initiatives to protect and preserve Ukrainian heritage — all these facts create new realities to which researchers have to adapt and within which they have to find new approaches to scientific activity. The experience of similar crises in other countries, as well as the experience of preserving archives in general, can be very useful for domestic science. Therefore, with the beginning of the war, institutions, foundations and associations from European countries, the USA and Canada began to actively offer their help in preserving Ukraine’s cultural heritage.
 The forced migration of researchers also brought a number of advantages, namely the strengthening of international contacts, the acquisition of work experience in foreign institutions by Ukrainian scientists, the presentation of domestic science to colleagues from other countries and thus the popularization of Ukrainian culture in the world. Throughout the war, Ukrainian researchers — musicologists and cultural activists — took part in the scientific and creative processes of various institutions in Poland, Lithuania, the Czech Republic, Slovenia, Austria, Germany, the Netherlands, France, Sweden, Canada, the USA, and other countries.
 The author of the article examines the history and principles of the organization and preservation of the Sound archive of the Institute of Ethnomusicology of the Research Center of the Slovenian Academy of Sciences and Arts (ZRC SAZU, Ljubljana), having the opportunity to work directly with this collection as a forcibly resettled researcher. The article gives a brief overview of the history of the Institute as the most important ethnomusicological center of Slovenia, examines the activities of key personalities of the Institute, their role in the history of the Institute and the archive, the approaches to collecting and studying the traditional music of Slovenes and outlines the current directions of work with the digital archive. Based on these facts, a comparison is made with traditional music archives in Ukraine and an assessment is given of the possibilities of incorporating the considered methods of organization and data storage into work with domestic collections of musical folklore, taking into account the urgency of such measures in times of increased risk of loss of cultural heritage due to the war.

Future of Blockchain: Data Storage, Carbon Calculation, Accounting, and Emissions Trading

The paper explores various aspects of data storage technologies, including Solid-State Storage, Cloud Computing, Edge Computing, and Blockchain, in the context of their implications, advantages, and limitations. Each technology's impact on data storage, performance, security, scalability, and environmental considerations is examined. Carbon Footprint (CF) is introduced as a measure of the environmental impact of data storage methods. The paper then delves into carbon calculation approaches, emphasizing the importance of accurate measurement in a computing environment. The integration of Carbon Footprint and Blockchain technology is discussed, presenting a framework for managing carbon emissions in data storage. Carbon accounting methodologies, including spend-based, activity-based, and hybrid methods, are detailed along with their applications in estimating greenhouse gas emissions. Finally, the paper explores the intersection of Carbon Emission Trading and Blockchain, highlighting how Blockchain's transparency and security attributes can address challenges within carbon trading systems.

Preservation of Sensitive Data Using Multi-Level Blockchain-based Secured Framework for Edge Network Devices

The proliferation of IoT devices has influenced end users in several aspects. Yottabytes (YB) of information are being produced in the IoT environs because of the ever-increasing utilization capacity of the Internet. Since sensitive information, as well as privacy problems, always seem to be an unsolved problem, even with best-in-class in-formation governance standards, it is difficult to bolster defensive security capabilities. Secure data sharing across disparate systems is made possible by blockchain technology, which operates on a decentralized computing paradigm. In the ever-changing IoT environments, blockchain technology provides irreversibility (immutability) usage across a wide range of services and use cases. Therefore, blockchain technology can be leveraged to securely hold private information, even in the dynamicity context of the IoT. However, as the rate of change in IoT networks accelerates, every potential weak point in the system is exposed, making it more challenging to keep sensitive data se-cure. In this study, we adopted a Multi-level Blockchain-based Secured Framework (M-BSF) to provide multi-level protection for sensitive data in the face of threats to IoT-based networking systems. The envisioned M-BSF framework incorporates edge-level, fog-level, and cloud-level security. At edge- and fog-level security, baby kyber and scaling kyber cryptosystems are applied to ensure data preservation. Kyber is a cryptosystem scheme that adopts public-key encryption and private-key decryption processes. Each block of the blockchain uses the cloud-based Argon-2di hashing method for cloud-level data storage, providing the highest level of confidentiality. Argon-2di is a stable hashing algorithm that uses a hybrid approach to access the memory that relied on dependent and independent memory features. Based on the attack-resistant rate (> 96%), computational cost (in time), and other main metrics, the proposed M-BSF security architecture appears to be an acceptable alternative to the current methodologies.

Inheritance of Intangible Cultural Heritage of Korean nationality Sports in Colleges and Universities in the Context of “One Belt, One Road”

Abstract This paper explores ways to preserve the intangible cultural heritage of Korean sports inherited by colleges and universities through digital technology and RBM neurons. The non-heritage information and images collected through the method of data collection and data storage are presented in 3D with the help of a neural network. The protection of intangible cultural heritage is achieved by using data restoration technology to trace the past and break the limitations of time and space. Using artificial intelligence technology, a database of intangible cultural heritage can be built, and different types of intangible cultural heritage can be divided using a neural network. The characteristics of Korean sports culture are summarized by analyzing intangible cultural items related to Korean sports. The results show that the feature extraction method incorporating RBM has the highest extraction rate of strong and healthy in sports, with an extraction rate of 0.8, the extraction rate of mighty features is 0.75, and the extraction rate of strength degree of sports is 0.75. The intangible culture of Korean sports is promoted and developed through this paper’s research to a certain extent.

Analysis and review of the possibility of using the generative model as a compression technique in DNA data storage: review and future research agenda

The amount of data in this world is getting higher, and overwriting technology also has severe challenges. Data growth is expected to grow to 175 ZB by 2025. Data storage technology in DNA is an alternative technology with potential in information storage, mainly digital data. One of the stages of storing information on DNA is synthesis. This synthesis process costs very high, so it is necessary to integrate compression techniques for digital data to minimize the costs incurred. One of the models used in compression techniques is the generative model. This paper aims to see if compression using this generative model allows it to be integrated into data storage methods on DNA. To this end, we have conducted a Systematic Literature Review using the PRISMA method in selecting papers. We took the source of the papers from four leading databases and other additional databases. Out of 2440 papers, we finally decided on 34 primary papers for detailed analysis. This systematic literature review (SLR) presents and categorizes based on research questions, namely discussing machine learning methods applied in DNA storage, identifying compression techniques for DNA storage, knowing the role of deep learning in the compression process for DNA storage, knowing how generative models are associated with deep learning, knowing how generative models are applied in the compression process, and knowing latent space can be formed. The study highlights open problems that need to be solved and provides an identified research direction.

The advance of non-digital-currency application with blockchain

With the vigorous development of blockchain technology, its decentralization, privacy, convenience, and other characteristics make it shine in the financial field. Today, blockchain technology has gradually been widely integrated into government, the Internet of Things, supply chain management, healthcare, and other areas. It can change the traditional centralized data management model and provide distributed and decentralized data storage and management methods. This method has higher security, transparency, and reliability, which can solve the problems existing in traditional data management methods and improve the efficiency and reliability of data management. In the meantime, using new technologies also brings many practical problems to solve. These include insufficient technology maturity, privacy concerns, and volatile legal and regulatory policies. This paper delves into these real-world scenarios and summarizes some recommendations. This will give a greater understanding of the principles and characteristics of blockchain technology, promote the popularization of blockchain technology, have a more comprehensive understanding of the use of blockchain technology in different fields, and provide valuable research materials for researchers in related fields in the future.

Optimizing data regeneration and storage with data dependency for cloud scientific workflow systems

The characters of large volume, complex dependency and frequent reuse of the data in data-intensive scientific workflow systems make the data management more and more complex but crucial. Automating the data management is an urgent need for scientific workflow systems and, requires accurate models and efficient methods for data storage and request response. This paper deals with the problems of data regeneration and storage optimization by using data dependency for cloud scientific workflow systems. Through representing the data storage strategies as 0-1 strings, a global discrete model for data storage optimization is constructed, which has a complexity of O(2n) (n is the dataset number). In terms of data regeneration, we develop a deterministic method to determine optimal regeneration strategies for requested datasets with minimal computation under any data storage strategy, such that the systems can satisfy the data requests automatically and evaluate the computation cost accurately. To solve this storage optimization problem, the enumeration method and elitist canonical genetic algorithm are constructed by shortening the representation of storage strategies to avoid redundant computation. At last, experiments are conducted to test the proposed methods. Experimental results and comparisons show the positiveness and effectiveness of the proposed methods for data management in cloud scientific workflow systems.

Implementation of Dynamic Virtual Cloud Architecture for Privacy Data Storage

Nowadays rapidly developing technologies, cloud computing offers versatile services. However, cloud computing presents a challenge to secure information sharing. Customers can securely share their data with others and remotely store it in the cloud using cloud storage services. In recent times, cloud storage typically represents as the primary method of external data storage. The primary challenge is safeguarding the cloud-based data against attacks. Over the information network, the growth of private or semi-private information has increased. The search techniques have not been addressed by privacy safeguards. As there is no suitable audit system, the validity of the stored data has become in question. In addition, user authentication presents additional difficulties. Hence in order to solve these issues, Design and implementation of dynamic virtual cloud architecture for privacy data storage is presented. In this approach, third-party audits are presented accompanied a new, regenerative public audit methodology. A distributed KDC (Key Distribution Center) is employed to encrypt the data. Documents can be stored on a private server in plain word form, which compromise the protection of privacy. As a result, system security can be improved to make the documents safer and more effective. The main objective of this Virtual Cloud Architecture is to achieve data confidentiality, as well as authenticity.

System-level data management for endpoint advanced persistent threat detection: Issues, challenges and trends

Advanced persistent threat (APT) attacks pose significant security threats to governments and large enterprises. Endpoint detection and response (EDR) methods, which are standard solutions to combat APT attacks, can efficaciously respond to associated security threats by leveraging the semantic richness of provenance graphs and clear causality relations to resist illegal tampering. However, the large number of audit logs produced over time, which provide key supporting information for EDR methods, lead to substantial computational overhead and increased storage costs. Therefore, a robust data management framework must be developed. However, most existing reviews discuss data collection, compression, and storage methods independently. Due to the lack of a comprehensive, structured survey of data management strategies, current data management analyses tend to be separated into individual modules, making it difficult to obtain prompt and precise guidance for higher-level security analysis tasks from these analyses. In this paper, a comprehensive and structured survey of data management strategies based on provenance graphs is conducted, the core ideas of the mainstream approaches to each aspect of data management are summarized, and existing approaches are systematically classified and compared. Then, the problems with individual data management modules are investigated, and potential complementary and collaborative strategies are examined based on the insights and challenges of existing work as a basis for recommending best practices for practical deployment. Finally, an ideal data management framework is described to guide future research.