Data Storage Solutions Research Articles

Secure cloud data storage solution with better data accessibility and time efficiency

The environment of cloud computing provides several advantages and a variety of data storage models, which entirely frees users from the vexing processes of storage equipment upgradation and data administration. Nevertheless, the customers’ primary worry is the safety of their data, and the literature offers a number of different security-based solutions for addressing this issue. This paper proposes a technique for time-efficient cloud data storage that makes use of keccak and Advanced Encryption Standard (AES) which enhances the data availability with a reasonable storage space. Third party agents are not employed, due to the fact that a third party cannot be relied upon to maintain a high level of data security. Thus, the proposed algorithm is applied by cloud users before the process of outsourcing is carried out. This method is resistant to data tamper and analytical attacks. In addition, the execution of the work that has been proposed requires a limited amount of time, which, in turn, minimizes the amount of energy that is required. In contrast to the already used algorithms, the presented work demonstrates superior performance in terms of its overall effectiveness.

Unified platform for storing, retrieving, and analysing biomechanical applications data using graph database

Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical quantities. Various sensors, measuring different parameters, can produce a large amount of data at high speeds and volumes that must be stored for real-time or post-processing and analysis. In addition to sensor data, metadata is an important component and can vary between biomechanical applications. Currently however, each application typically has its own unique data flow and storage solution. In this research, we present a universal data model solution that can be applied to any sensor-based biomechanical application in sport and physical rehabilitation. Our proposed cloud platform architecture allows for the manipulation of sensor data and metadata using a combination of Big Data and conventional techniques. The main idea of this research is to develop a platform that allows a universal way for any biomechanical application to handle its data regardless of the type of data and metadata. This is achieved by creating a universal data model, and implementing this data model in a generalized architecture using a graph database. We demonstrate the benefits of this approach using examples from existing biomechanical systems and describe the development of the cloud platform architecture and the underlying data model. We also provide an example of the use of this platform in a sport shooting application. This approach is unique in that it allows data from different sources and applications to be stored and processed using the same procedures and techniques, facilitating data analysis and application development. We envision this system will expand to multiple different biomechanical applications in the future. We expect that in time, the ability to compare various data and store different biomechanical datasets will become necessity. With the advantages of modern recommender systems and utilization of artificial intelligence, huge amounts of relevant and well-prepared data with useful metadata are required thus having such system is an important advantage for future biomechanical systems development. With the increase of people’s awareness and usage of devices that increase well-being and quality of life, presented platform and similar systems will play a pivotal role in shaping the future lifestyle.

Modelling, Characterization of Data-Dependent and Process-Dependent Errors in DNA Data Storage.

Using DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process. In this work, we first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process.

Data Warehouse Failover Cluster for Analytical Queries in Banking

Introduction. The banking sector assigns high priority to data storage, as it is a critical aspect of business operations. The volume of data in this area is steadily growing. With the increasing volume of data that needs to be stored, processed and analyzed, it is critically important to select a suitable data storage solution and develop the required architecture. The presented research is aimed at filling the gap in the existing knowledge of the data base management system (DBMS) suitable for the banking sector, as well as to suggest ways for a fault-tolerant data storage cluster. The purpose of the work is to analyze the key DBMS for analytical queries, determine the priorities of the DBMS for the banking sector, and develop a fault-tolerant data storage cluster. To meet the performance and scalability requirements, a data storage solution with a fault-tolerant architecture that meets the requirements of the banking sector has been proposed.Materials and Methods. Domain analysis allowed us to create a set of characteristics that a DBMS for analytical queries (OnLine Analytical processing — OLAP) should correspond to, compare some popular DBMS OLAP, and offer a fault-tolerant cluster configuration written in xml, supported by the ClickHouse DBMS. Automation was done using Ansible Playbook. It was integrated with the Gitlab version control system and Jinja templates. Thus, rapid deployment of the configuration on all nodes of the cluster was achieved.Results. For OLAP databases, criteria were developed and several popular systems were compared. As a result, a reliable cluster configuration that met the requirements of analytical queries has been proposed for the banking industry. To increase the reliability and scalability of the DBMS, the deployment process was automated. Detailed diagrams of the cluster configuration were also provided.Discussion and Conclusions. The compiled criteria for the DBMS OLAP allowed us to determine the need for this solution in the organization. Comparison of popular DBMS can be used by organizations to minimize costs when selecting a solution. The proposed configuration of the data warehouse cluster for analytical queries in the banking sector will improve the reliability of the DBMS and meet the requirements for subsequent scalability. Automation of cluster deployment by the mechanism of templating configuration files in Ansible Playbook provides configuring a ready-made cluster on new servers in minutes.

Supply chain traceability using blockchain

In the current global marketplace supply chains can span a huge number of countries, cross many borders and require interoperation of a multitude of organizations. This vastness of supply chains impacts business competitiveness since it adds complexity and can difficult securing traceability, chain of custody and transparency. We propose that assuring chain of custody and traceability via Blockchain (BC) allows organizations to demonstrate product provenance, integrity and compliance. This work proposes that to effect true traceability the more complete approach is to connect both the Supply Chain Actors (SCAs) and products identifications using digital certificates. A Blockchain is used to manage the traceability of products and validation of the identities. Importing, verifying and storing the certificates uses an off-chain data storage solution for products certificates, IDs and data (i.e., WalliD) . To create, validate the certificates and setup the chain of trust a Public Key Infrastructure (PKI) was designed as part of the proposal. Our study follows a Design Science research approach aimed to analyse the requirements and propose a solution to a more complete traceability in supply chains. The results were architectural artifacts, including an Ethereum Smart Contract and a PKI based certificate authentication system. The implementation of these deliverables allow for a supply chain system that can provide decentralized and trustful assurance of the provenance, chain of custody and traceability functionalities for all the Organizations and also for the final consumers.To exemplify the problem and demonstrate the applicability of the solution, its potential and benefits we applied it to a real food supply chain use case that already uses provenance certificates and stored them in the blockchain using the before mentioned SmartContract to assure and demonstrate the chain of custody and traceability of the food produce.

GLEAN: Generalized-Deduplication-Enabled Approximate Edge Analytics

The Internet of Things (IoT) has brought about exponential growth in sensor data. This has led to increasing demands for efficient and novel data transmission, storage, and analytics solutions for sustainable IoT ecosystems. It has been shown that the generalized deduplication (GD) compression algorithm offers not only competitive compression ratio and throughput but also random access properties that enable direct analytics of compressed data. In this article, we thoroughly stress test existing methods for direct analytics of GD compressed data with a diverse collection of 103 data sets, identify the need to optimize GD for analytics, and develop a new version of GD to this end. We also propose the generalized deduplication-enabled approximate edge analytics (GLEAN) framework. This framework applies the aforementioned analytics techniques at the Edge server to deliver end-to-end lossless data compression and high-quality Edge analytics in the IoT, thereby addressing challenges related to data transmission, storage, and analytics. Impressive analytics performance was achieved using this framework, with a median increase in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering error of just 2% relative to analytics performed on uncompressed data, while running <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.5\times $ </tex-math></inline-formula> faster and requiring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.9\times $ </tex-math></inline-formula> less storage at the Edge server compared to universal compressors.

Facilitating Serverless Match-based Online Games with Novel Blockchain Technologies

Applying peer-to-peer (P2P) architecture to online video games has already attracted both academic and industrial interests, since it removes the need for expensive server maintenance. However, there are two major issues preventing the use of a P2P architecture, namely how to provide an effective distributed data storage solution, and how to tackle potential cheating behaviors. Inspired by emerging blockchain techniques, we propose a novel consensus model called Proof-of-Play (PoP) to provide a decentralized data storage system that incorporates an anti-cheating mechanism for P2P games, by rewarding players that interact with the game as intended, along with consideration of security measures to address the Nothing-at-stake Problem and the Long-range Attack. To validate our design, we utilize a game-theory model to show that under certain assumptions, the integrity of the PoP system would not be undermined due to the best interests of any user. Then, as a proof-of-concept, we developed a P2P game ( Infinity Battle ) to demonstrate how a game can be integrated with PoP in practice. Finally, experiments were conducted to study PoP in comparison with Proof-of-Work (PoW) to show its advantages in various aspects.

An HBase-Based Optimization Model for Distributed Medical Data Storage and Retrieval

In medical services, the amount of data generated by medical devices is increasing explosively, and access to medical data is also put forward with higher requirements. Although HBase-based medical data storage solutions exist, they cannot meet the needs of fast locating and diversified access to medical data. In order to improve the retrieval speed, the recognition model S-TCR and the dynamic management algorithm SL-TCR, based on the behavior characteristics of access, were proposed to identify the frequently accessed hot data and dynamically manage the data storage medium as to maximize the system access performance. In order to improve the search performance of keys, an optimized secondary index strategy was proposed to reduce I/O overhead and optimize the search performance of non-primary key indexes. Comparative experiments were conducted on real medical data sets. The experimental results show that the optimized retrieval model can meet the needs of hot data access and diversified medical data retrieval.

Multitask Scheduling on Distributed Cloudlet System Built Using SoCs

With the emergence of IoT, new computing paradigms have also emerged. Initial IoT systems had all the computing happening on the cloud. With the emergence of Industry 4.0 and IoT being the major building block, clouds are not the only solution for data storage and analytics. Cloudlet, Fog Computing, Edge Computing, and Dew Computing models are now available, providing similar capabilities as the cloud. The term cloudlet was introduced first in 2011, but research in this area has picked up only over the past five years. Unlike clouds, which are built with powerful server-class machines and GPUs, cloudlets are usually made using simpler devices such as SoCs. In this paper, we propose a complete novel distributed architecture for cloudlets, and we are also proposing algorithms for data storage and task allocation across various nodes in the cloudlet. This cloudlet system was built using Qualcomm Snapdragon 410c. We have analyzed the architecture and the algorithm for varying workloads, bandwidth and data storage. The primary aim of the algorithm and the architecture is to ensure uniform processing and data loads across the nodes of the system.

Massive Data Storage Solution for IoT Devices Using Blockchain Technologies.

The Internet of Things (IoT) concept involves connecting devices to the internet and forming a network of objects that can collect information from the environment without human intervention. Although the IoT concept offers some advantages, it also has some issues that are associated with cyber security risks, such as the lack of detection of malicious wireless sensor network (WSN) nodes, lack of fault tolerance, weak authorization, and authentication of nodes, and the insecure management of received data from IoT devices. Considering the cybersecurity issues of IoT devices, there is an urgent need of finding new solutions that can increase the security level of WSNs. One issue that needs attention is the secure management and data storage for IoT devices. Most of the current solutions are based on systems that operate in a centralized manner, ecosystems that are easy to tamper with and provide no records regarding the traceability of the data collected from the sensors. In this paper, we propose an architecture based on blockchain technology for securing and managing data collected from IoT devices. By implementing blockchain technology, we provide a distributed data storage architecture, thus eliminating the need for a centralized network topology using blockchain advantages such as immutability, decentralization, distributivity, enhanced security, transparency, instant traceability, and increased efficiency through automation. From the obtained results, the proposed architecture ensures a high level of performance and can be used as a scalable, massive data storage solution for IoT devices using blockchain technologies. New WSN communication protocols can be easily enrolled in our data storage blockchain architecture without the need for retrofitting, as our system does not depend on any specific communication protocol and can be applied to any IoT application.

Load Redistribution-based Reliability Enhancement for Storage Area Networks

Storage area networks (SANs) are one of the prevalent reliable data storage solutions. However, cascading failures triggered by data overloading have become a major threat to SANs, preventing the desired quality of service from being delivered to users. Based on our preliminary works on studying the impacts of data loading on the reliability performance of SANs, this paper advances the state of the art by implementing node degree-based load redistribution strategies to enhance the SAN reliability, thus mitigating or even preventing the occurrence of cascading failures during the mission time. Load-based and reliability-based node selection rules are considered, which choose nodes with the highest load level and the lowest reliability for load redistribution, respectively. The relationship between data loading and reliability of an individual SAN component is modeled using the accelerated failure-time model with the power law. The SAN reliability is assessed using a combinatorial decision diagram-based approach. The application and effectiveness of the proposed load redistribution strategies are demonstrated and compared through a case study of an SAN with the mesh topology.

Performance Evaluation of Cryptographic Security Algorithms on Cloud

Cloud computing is a cost-effective approach to provide on demand computing and data storage solutions. Data storage is one of the key services provided by the cloud. Cloud offers improved efficiency, flexibility, and scalability, but all these advantages can be overturned if security is not taken into consideration. It is the cloud vender’s responsibility to keep the data safe with highly secure cloud services, which helps them earn the trust of the customers. Today, cloud security is critical since most organizations are already using cloud computing in alternate forms. There is always a concern that the highly sensitive business information and intellectual property may be exposed or misused due to increasingly sophisticated cyber threats. This research paper provides a distributed architecture for cloud data security which is independent of the underlying platforms. A cloud security architecture provides written and visual model to define how to configure and secure operations within the cloud. This paper compares the performance of RC4 against AES-256. The performance of the proposed encryption algorithms is evaluated on a widely used database MySQL. This paper provides a better solution to ensure the security of cloud databases by using two encryption algorithms.

A Survey on Blockchain for Healthcare: Challenges, Benefits, and Future Directions

Continuously generated volumes of health data make healthcare a data-intensive domain. This data needs to be collected, stored, and shared among different healthcare actors for various purposes, such as reporting, analysis, collaborative research, and personalized healthcare services. However, the existing data storage and exchange solutions in the healthcare domain exhibit several challenges related to, e.g., data security, patient privacy, and interoperability. Recently, the industry and research community turned its focus to the possible use of blockchain technology to solve some of these challenges in the healthcare domain. The blockchain technology along with the support from smart contracts is considered a salient facilitator for secure and efficient health data sharing. This is due to its unique features, such as decentralization, trustlessness, immutability, traceability, and transparency. In this paper, we provide a comprehensive survey of the state-of-the-art efforts that envision the use of blockchain-based solutions in the healthcare domain. To this end, we introduce a systematic framework for classifying and analyzing such systems. The framework consists of classification in several dimensions: interactions between healthcare entities, functional components of healthcare storage systems, challenges in the healthcare domain that can be overcome by using the blockchain technology, and benefits for healthcare storage systems derived from the fundamental features of the technology. When analyzing over 40 systems and solutions proposed in the state-of-the-art, we perform their rigorous placement by identifying the exact scope of each solution and mapping it to the above taxonomies of interactions, functional components, challenges, and benefits. We additionally provide an extensive discussion of compliance with privacy-related regulations of General Data Protection Regulation (GDPR) in EU, and Health Insurance Portability and Accountability Act (HIPAA). Following the results of the analysis, we have outlined a number of important research gaps and future directions yet to be addressed.

DNA technology for big data storage and error detection solutions: Hamming code vs Cyclic Redundancy Check (CRC)

There is an increasing need for high-capacity, highdensity storage media that can retain data for a long time, due to the exponential development in the capacity of information generated. The durability and high information density of synthetic deoxyribonucleic acid (DNA) make it an attractive and promising medium for data storage. DNA data storage technology is expected to revolutionize data storage in the coming years, replacing various Big Data storage technologies. As a medium that addresses the need for high-latency, immutable information storage, DNA has several potential advantages. One of the key advantages of DNA storage is its extraordinary density. Theoretically, a gram of DNA can encode 455 exabytes, or 2 bits per nucleotide. Unlike other digital storage media, synthetic DNA enables large quantities of data to be stored in a biological medium. This reduces the need for traditional storage media such as hard disks, which consume energy and require materials such as plastic or metals, and also often leads to the generation of electronic waste when they become obsolete or damaged. Additionally, although DNA degrades over thousands of years under non-ideal conditions, it is generally readable. Furthermore, as DNA possesses natural reading and writing enzymes as part of its biological functions, it is expected to remain the standard for data retrieval in the foreseeable future. However, the high error rate poses a significant challenge for DNA-based information coding strategies. Currently, it is impossible to execute DNA strand synthesis, amplification, or sequencing errors-free. In order to utilize synthetic DNA as a storage medium for digital data, specialized systems and solutions for direct error detection and correction must be implemented. The goal of this paper is to introduce DNA storage technology, outline the benefits and added value of this approach, and present an experiment comparing the effectiveness of two error detection and correction codes (Hamming and CRC) used in the DNA data storage strategy.

Retracted] Storage Method for Medical and Health Big Data Based on Distributed Sensor Network

Monitoring and collecting medical data using embedded medical diagnostic devices with multiple sensors and sending these actual measured data to the corresponding health monitoring centers using multipurpose wireless networks to take necessary measures to coordinate with family medical service centers and regional medical service departments is a popular medical big data architecture. However, healthcare big data is characterized by large data volume, fast growth, multimodality, high value and privacy, etc. How to organize and manage it in a unified and efficient way is an important research direction at present. In response to the problems of low balance and poor security in the storage of data collected by distributed sensor networks in healthcare systems, we propose a distributed storage algorithm for big data in healthcare systems. The platform adopts Hadoop distributed file system and distributed file storage framework as the healthcare big data storage solution, and implements data integration, multidimensional data query and analysis mining components based on Spark‐SQL data query tool, Spark machine learning algorithm library and its mining and analysis pipeline development, respectively. The distributed storage model of big data and three data storage levels are constructed using cloud storage architecture, and the data storage intensity as well as levels are calculated by high data access in the upper level, data connection in the middle level, and data archiving in the lower level according to the set known data granularity, odds, and elasticity to realize big data storage. It is experimentally verified that the above algorithm has high distribution balance and low load balance in the storage process.

A SURVEY A HYBRID MODEL FOR CENTRAL BANK DIGITAL CURRENCY BASED ON BLOCKCHAIN

With the development of blockchain technology, research on digital currency is gaining popularity, especially Central Bank Digital Currency (CBDC), which is essential to the growth of a nation's economy. However, CBDC requires a more complicated system than other crypto currencies do. a stronger focus on supervision and controllable decentralization Therefore, the technical foundation that is in accordance with economic ecology, efficient consensus methods, and network architecture that conserves computer resources are essential elements of CBDC. In this study, we propose a modularity network-enabled hybrid blockchain system for CBDC. Common digital currencies are recorded using the account system, especially for numerous small payment transactions, whereas digital assets and smart contracts with major value fluctuations and low liquidity are recorded using the Unspent Transaction Output (UTXO) scheme. To boost the concurrency of this structured network, a sliced data storage solution is developed, and a modular blockchain design is recommended. The DPOS-BFT technique is enhanced based on the block chain’s CBDC supervision mode, which condenses the original algorithm's two rounds of consensus into a single round. The effectiveness of this approach in speeding up consensus and transaction processing is shown in three simulated studies on scheme, network, and consensus.

Leveraging Azure Data Lake for Efficient Data Processing in Telematics

In the telematics industry, the continuous generation of large volumes of data presents significant challenges in terms of storage, processing, and analysis. Azure Data Lake, a scalable and secure data storage solution, offers an efficient platform to handle these massive datasets. This paper explores the application of Azure Data Lake in telematics for efficient data processing, focusing on its capacity to store vast amounts of structured and unstructured data while providing seamless integration with various analytics tools. By leveraging Azure Data Lake, organizations can enhance their data processing capabilities, improve decision-making, and reduce operational costs. The study investigates how Azure Data Lake simplifies data management through its high availability and accessibility, allowing businesses to manage data from multiple sources with minimal complexity. Furthermore, the integration with Azure services like Azure Data Factory and Azure Databricks facilitates advanced analytics, enabling real-time insights and predictive analytics, which are crucial for the telematics sector. The findings suggest that adopting Azure Data Lake improves data processing efficiency, enhances scalability, and supports the development of innovative telematics applications such as fleet management and vehicle monitoring systems. The paper concludes by highlighting the potential of Azure Data Lake to revolutionize the telematics industry by enabling more agile and data-driven operations.

A secured big-data sharing platform for materials genome engineering: State-of-the-art, challenges and architecture

Materials are the foundation of social development. The vigorous development of big-data technology has brought new opportunities for material research and development, gradually entering the data-driven paradigm. How to safely collect, store and utilize material big-data to realize the design and prediction of advanced materials has essential research significance and value. Many material big-data platforms have been constructed to gather multi-source heterogeneous material data. However, these traditional platforms are hard to realize the safe and efficient circulation and utilization of data. Relying on the national Materials Genome Engineering (MGE) project, we built a secured big-data sharing platform and proposed corresponding data collection, storage, utilization, and security solutions. On the one hand, the blockchain framework working as a ‘middleware’ provides a standard application program interface for data interaction between participants, and participants do not need to perceive the underlying system framework; on the other hand, it provides a unified management and security mechanism for the platform. In terms of collection, the dynamic container model is used to solve the data normalization problem, thereby improving data quality. In terms of storage, data adaptors store normalized data in different databases for distributed storage and unified scheduling. The platform provides a unified service gateway to schedule all services for data utilization. The secured big-data sharing platform can improve data utilization, promote material data sharing, accelerate material discovery, and serve the data needs of high-throughput computing and the design of new materials.

Data Marketplaces: A Solution for Personal Data Control and Ownership?

Data sharing is critical for advancing research, commercializing technologies, and informing people. However, data owners have been reluctant to share data due to concerns about data control, access, and a lack of secure data storage solutions. Frequent data breaches have also led to a lack of user trust in businesses. Despite widespread recognition of user concerns, few steps have been taken to empower users. While there are models for decentralized data sharing, the question of how to incentivize these structures to enable data sharing at scale is largely unexplored. This work discusses the potential of blockchain-based data marketplaces to empower users by giving them more control over their data and bringing transparency to the data-driven ecosystem.

RETRACTED: Bionic‐structure thermo‐responsive (best) hydrogels with controllable layer for high‐capacity DNA data storage

AbstractHere, a new‐rising technology (DNA storage) in which the binary digital information/data is converted into a DNA sequence composed of distinct nucleotides, providing a dense, stable, energy‐efficient, and sustainable data storage solution is studied. In principle, this technology offers substantial data density. However, the theoretical limit of DNA storage has not been achieved yet. In this study, a new DNA storage system is proposed based on hydrogels with SiO2 layer (their pore size can be regulated). The highest data density of the hydrogels is obtained to be 6.3 × 109 GB g−1. Further, by coupling them with digital polymerase chain reaction (dPCR), the recovery limit reaches five copies of DNA. Subsequently, three‐dimensional (3D) printing is used to explore the effect of macrostructures on the DNA storage capacity. Finally, bionic‐structure thermally‐responsive robust (Best) hydrogels with four different structures are developed, which achieve a data density of 1.04 × 1010 GB g−1.