Search

In the screening phase of a systematic review, screening prioritization via active learning effectively reduces the workload. However, the PRISMA guidelines are not sufficient for reporting the screening phase in a reproducible manner. Text screening with active learning is an iterative process, but the labeling decisions and the training of the active learning model can happen independently of each other in time. Therefore, it is not trivial to store the data from both events so that one can still know which iteration of the model was used for each labeling decision. Moreover, many iterations of the active learning model will be trained throughout the screening process, producing an enormous amount of data (think of many gigabytes or even terabytes of data), and machine learning models are continually becoming larger. This article clarifies the steps in an active learning-aided screening process and what data is produced at every step. We consider what reproducibility means in this context and we show that there is tension between the desire to be reproducible and the amount of data that is stored. Finally, we present the RDAL Checklist (Reproducibility and Data storage for Active Learning-Aided Systematic Reviews Checklist), which helps users and creators of active learning software make their screening process reproducible.

The contemporary surge in data production is fueled by diverse factors, with contributions from numerous stakeholders across various sectors. Comparing the volumes at play among different big data entities is challenging due to the scarcity of publicly available data. This survey aims to offer a comprehensive perspective on the orders of magnitude involved in yearly data generation by some public and private leading organizations, using an array of online sources for estimation. These estimates are based on meaningful, individual data production metrics and plausible per-unit sizes. The primary objective is to offer insights into the comparative scales of major big data players, their sources, and data production flows, rather than striving for precise measurements or incorporating the latest updates. The results are succinctly conveyed through a visual representation of the relative data generation volumes across these entities.

Graph neural networks (GNNs) have achieved great success in processing non-Euclidean geometric spatial data structures. However, the irregular memory access of aggregation and the power-law distribution of the real-world graph challenge the existing memory hierarchy and caching policy of CPUs and GPUs. Meanwhile, after the emergence of an increasing number of GNN algorithms, higher requirements have been established for the flexibility of the hardware architecture. In this work, we design a dynamically reconfigurable GNN accelerator (named DRGN) supporting multiple GNN algorithms. Specifically, we first propose a vertex reordering algorithm and an adjacency matrix compressing algorithm to improve the graph data locality. Furthermore, to improve bandwidth utilization and the reuse rate of node features, we proposed a dedicatedly designed prefetcher to significantly improve hit rate. Finally, we proposed a scheduling mechanism to assign tasks to PE units to address the issue of workload imbalance. The effectiveness of proposed DRGN accelerator was evaluated using three GNN algorithms, including PageRank, GCN, and GraphSage. Compared to the execution time of these three GNN algorithms on CPU, performing PageRank algorithm on DRGN can achieve speedup by 231×, the GCN algorithm can achieve speedup by 150× on DRGN, and the GraphSage algorithm can achieve speedup by 39× when executed on DRGN. Compared with state-of-the-art GNN accelerators, DRGN can achieve higher energy-efficiency under the condition of relative lower-end process. [ABSTRACT FROM AUTHOR]

Generally, enterprises store data in internal storage and establish firewalls to protect against intruders attempting to access the data. In cloud computing, data is stored in storage provided by service providers. Information privacy protection involves storing data in encrypted form. The cloud system is responsible for both storage and the encryption or decryption of data, allowing system administrators to access encrypted data and decryption keys. This capability poses a risk to information privacy as it enables unauthorized access to information. This survey discusses Data Storage in Cloud Computing, focusing on the concept of storing encryption and decryption services alongside the storage service. Additionally, the survey examines storage system architecture, security challenges, services, and user application systems to provide a comparison of various cloud models. In addition to the fundamental aspects of data storage and encryption, this survey delves into the evolving landscape of cloud computing, addressing the dynamic nature of storage system architectures. Enterprises increasingly rely on cloud service providers for scalable and flexible storage solutions, prompting a critical examination of the security challenges associated with this paradigm shift. The survey examines how storage services in different cloud models affect user applications and privacy. It stresses the importance of understanding cloud providers' services amidst evolving technology and growing threats. The study delves into trends like edge computing and distributed storage, evaluating their impact on cloud storage security. Overall, it aims to provide a comprehensive overview for enterprises and researchers to make informed decisions on data security in Cloud Computing. [ABSTRACT FROM AUTHOR]

Biobanks are driving motors of precision and personalized medicine by providing high-quality biological material/data through the standardization and harmonization of their collection, preservation, and distribution. UPO Biobank was established in 2020 as an institutional, disease, and population biobank within the University of Piemonte Orientale (UPO) for the promotion and support of high-quality, multidisciplinary studies. UPO Biobank collaborates with UPO researchers, sustaining academic translational research, and supports the Novara Cohort Study, a longitudinal cohort study involving the population in the Novara area that will collect data and biological specimens that will be available for epidemiological, public health, and biological studies on aging. UPO Biobank has been developed by implementing the quality standards for the field and the ethical and legal issues and normative about privacy protection, data collection, and sharing. As a member of the "Biobanking and Biomolecular Resources Research Infrastructure" (BBMRI) network, UPO Biobank aims to expand its activity worldwide and launch cooperation with new national and international partners and researchers. The objective of this manuscript is to report an institutional and operational experience through the description of the technical and procedural solutions and ethical and scientific implications associated with the establishment of this university research biobank. [ABSTRACT FROM AUTHOR]

DNA-based data storage is a rapidly evolving technology that could solve the rising energy and space needs of current information storage. However, DNA's stability limits its potential for future data storage systems. To address limitations to the current working storage method, we developed a novel method to store DNA in an aqueous solution with enhanced stability. Forming a Transcription Factor A Mitochondria (TFAM)-DNA complex in an aqueous solution, where TFAM encapsulates the DNA to protect it from UV irradiation and oxidation stress, could enhance the stability of DNA during storage and retrieval. First, we cloned the TFAM gene, overexpressed the TFAM protein, and purified the TFAM protein from E. coli. Next, we designed a smiley face pixel information in a black and white image (10x10). Then, we converted the data into binary numbers (white=0, black=1). The 10x10 binary numbers were then switched to nucleotides, with zero being either G or C and one being A or T. The image was encoded into nucleotides, synthesized into DNA, and cloned into a plasmid vector. After each stress was induced, the cloned DNA plasmid was sequenced and decoded back to the original image. Our result shows that the TFAM-DA complex provides a stable storage method even with harsh UV irradiation and oxidative stress conditions in aqueous solutions. In conclusion, our storage method's novelty allows for frequent access to stored data in an aqueous solution and provides protection from external stress. [ABSTRACT FROM AUTHOR]

In real-time data-intensive applications, achieving real-time data acquisition from sensors and simultaneous storage with the necessary performance is challenging, especially if "no-data-lost" requirements are present. Ad hoc solutions are generally expensive and suffer from a lack of modularity and scalability. In this work, we present a hardware/software platform built using commercial off-the-shelf elements, designed to acquire and store digitized signals captured from imaging spectrometers capable of supporting real-time data acquisition with stringent throughput requirements (sustained rates in the boundaries of 100 MBytes/s) and simultaneous information storage in a lossless fashion. The correct combination of commercial hardware components with a properly configured and optimized multithreaded software application has satisfied the requirements in determinism and capacity for processing and storing large amounts of information in real time, keeping the economic cost of the system low. This real-time data acquisition and storage system has been tested in different conditions and scenarios, being able to successfully capture 100,000 1 Mpx-sized images generated at a nominal speed of 23.5 MHz (input throughput of 94 Mbytes/s, 4 bytes acquired per pixel) and store the corresponding data (300 GBytes of data, 3 bytes stored per pixel) concurrently without any single byte of information lost or altered. The results indicate that, in terms of throughput and storage capacity, the proposed system delivers similar performance to data acquisition systems based on specialized hardware, but at a lower cost, and provides more flexibility and adaptation to changing requirements. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Blockchain is a distributed database based on the Bitcoin platform.It has attracted widespread attention from academia and industry because of its characteristics of decentralization,traceability,and non-tamperability.However,with the rapid growth of the number of nodes and the amount of data,problems such as low throughput and difficult storage scalability appear.The problem of storage scalability of blockchain has become a bottleneck for blockchain applications.Therefore,this paper focuses on the summary and prospect of storage scalability of blockchain.Firstly,this paper introduces the basic knowledge and data structure of blockchain and analyzes the problems of storage scalability of blockchain at present.Secondly,this paper explains the principles,implementation methods,research progress,advantages,and disadvantages of the 0th layer expansion scheme,the on-chain expansion scheme,and the off-chain expansion scheme.Finally,this paper analyzes the challenges in the current blockchain sto-ragescalability scheme and provides a direction for the next blockchain research work.

The output speed of a high-resolution camera with a fast frame rate is high, reaching 800 MB/s or even more. Consequently, the abundant data recorded can easily cause data overflow during transmission and frame loss during storage. To address this problem of data overflow and frame loss while achieving reliable acquisition rates, herein, we design a high-speed video adaptive transmission and storage method based on the camera link transmission protocol. The system uses the camera link transmission protocol and high-speed cameras for data acquisition. During data transmission, memory space is employed as the data buffer pool, an adaptive memory buffer pool is designed using the hardware and acquisition parameters, and a circular read/write buffer, combined with a block device read/write method, is used inside the buffer pool for data transmission. For data storage, input/output completion port asynchronous storage mixed with frame sequence number filtering is employed to write data, and the adaptive buffer of the hard disk is used to achieve efficient data writing. The experimental results reveal that the proposed method can realize efficient and stable writing of data on conventional industrial cameras based on the camera link interface, effectively addressing the problem of frame loss during data storage. Moreover, the stored data can be transferred efficiently with less resource occupation, thereby meeting the application requirements of high-precision vision measurement systems and demonstrating broad application value.

In an ever-growing need for data storage capacity, the Deoxyribonucleic Acid (DNA) molecule gains traction as a new storage medium with a larger capacity, higher density, and a longer lifespan over conventional storage media. To effectively use DNA for data storage, it is important to understand the different methods of encoding information in DNA and compare their effectiveness. This requires evaluating which decoded DNA sequences carry the most encoded information based on various attributes. However, navigating the field of coding theory requires years of experience and domain expertise. For instance, domain experts rely on various mathematical functions and attributes to score and evaluate their encodings. To enable such analytical tasks, we provide an interactive and visual analytical framework for multi-attribute ranking in DNA storage systems. Our framework follows a three-step view with user-settable parameters. It enables users to find the optimal en-/de-coding approaches by setting different weights and combining multiple attributes. We assess the validity of our work through a task-specific user study on domain experts by relying on three tasks. Results indicate that all participants completed their tasks successfully under two minutes, then rated the framework for design choices, perceived usefulness, and intuitiveness. In addition, two real-world use cases are shared and analyzed as direct applications of the proposed tool. DNAsmart enables the ranking of decoded sequences based on multiple attributes. In sum, this work unveils the evaluation of en-/de-coding approaches accessible and tractable through visualization and interactivity to solve comparison and ranking tasks.

Natural DNA storage allows cellular differentiation, evolution, the growth of our children and controls all our ecosystems. Here, we discuss the fundamental aspects of DNA storage and recent advances in this field, with special emphasis on natural processes and solutions that can be exploited. We point out new ways of efficient DNA and nucleotide storage that are inspired by nature. Within a few years DNA-based information storage may become an attractive and natural complementation to current electronic data storage systems. We discuss rapid and directed access (e.g. DNA elements such as promotors, enhancers), regulatory signals and modulation (e.g. lncRNA) as well as integrated high-density storage and processing modules (e.g. chromosomal territories). There is pragmatic DNA storage for use in biotechnology and human genetics. We examine DNA storage as an approach for synthetic biology (e.g. light-controlled nucleotide processing enzymes). The natural polymers of DNA and RNA offer much for direct storage operations (read-in, read-out, access control). The inbuilt parallelism (many molecules at many places working at the same time) is important for fast processing of information. Using biology concepts from chromosomal storage, nucleic acid processing as well as polymer material sciences such as electronical effects in enzymes, graphene, nanocellulose up to DNA macramé , DNA wires and DNA-based aptamer field effect transistors will open up new applications gradually replacing classical information storage methods in ever more areas over time (decades).

The Internet of Things (IoT) is currently reflected in the increase in the number of connected objects, that is, devices with their own identity and computing and communication capacities. IoT is recognized as one of the most critical areas for future technologies, gaining worldwide attention. It applies to many areas, where it has achieved success, such as healthcare, where a patient is monitored using nodes and lightweight sensors. However, the powerful functions of IoT in the medical field are based on communication, analysis, processing, and management of data autonomously without any manual intervention, which presents many difficulties, such as energy consumption. However, these issues significantly slow down the development and rapid deployment of this technology. The main causes of wasted energy from connected objects include collisions that occur when two or more nodes send data simultaneously and the leading cause of data retransmission that occurs when a collision occurs or when data are not received correctly due to channel fading. The distance between nodes is one of the factors influencing energy consumption. In this article, we have proposed direct communication between nodes to avoid collision domains, which will help reduce data retransmission. The results show that the distribution can ensure the performance of the system under general conditions compared to the centralization and to the existing works.

The risk of a catastrophic or existential disaster for our civilization is increasing this century. A significant motivation for a near-term space settlement is the opportunity to safeguard civilization in the event of a planetary-scale disaster. A catastrophic event could destroy the significant cultural, scientific, and technological progress on Earth. However, early space settlements can preserve records of human activity by maintaining a backup data storage system. The backup can also store information about the events leading up to the disaster. The system would improve the ability of early space settlers to recover our civilization after collapse. We show that advances in laser communications and data storage enable the development of a data storage system on the lunar surface with a sufficient uplink data rate and storage capacity to preserve valuable information about the achievements of our civilization and the chronology of the disaster.

Abstract In recent years the process of transportation needs a highly effective traffic system in order to monitor all consumer goods as many goods are left out at different locations. To handle such moving cases cloud platform is highly helpful as with respect to geographical location the goods are mapped in correct form. However incorporation of single cloud platform does not provide sufficient amount of storage about all goods thus a multiple cloud platform is introduced in proposed system. As multiple cloud platform is provided the security features of each data base system is also checked and enhanced using encryption keys. Moreover for proper operating conditions of multiple cloud platforms an analytical model is designed that synchronizes necessary data at end system. The defined analytical model focuses on solving multiple objectives that are related to critical energy problems where demand problems are reduced. Further the encryption process is carried out using Improved BlowFish Algorithm (IBFA) by allocating proper resources with decryption keys. To validate the effectiveness of proposed method five scenarios are considered where all scenario outcomes proves to be much higher than existing models by an average of 43%.

A distributed high-speed data acquisition and monitoring system for the RF negative ion source at Huazhong University of Science and Technology (HUST) is developed, which consists of data acquisition, data forwarding and data processing. Firstly, the data acquisition modules sample physical signals at high speed and upload the sampling data with corresponding absolute-time labels over UDP, which builds the time correlation among different signals. And a special data packet format is proposed for the data upload, which is convenient for packing or parsing a fixed-length packet, especially when the span of the time labels in a packet crosses an absolute second. The data forwarding modules then receive the UDP messages and distribute their data packets to the real-time display module and the data storage modules by PUB/SUB-pattern message queue of ZeroMQ. As for the data storage, a scheme combining the file server and MySQL database is adopted to increase the storage rate and facilitate the data query. The test results show that the loss rate of the data packets is within the range of 0–5% and the storage rate is higher than 20 Mbps, both acceptable for the HUST RF negative ion source.

A lot of scientific work is dedicated to the analysis of data. Most of the analyzed data, like data from space missions, are structured. The choice of data format can affect various characteristics - read/write speed of standard files, read/write speed of small files and read/write speed of compressed data formats. In this paper, we analyze binary data formats, proposed types of the tests and testing methods, and compare their performance with human-readable text format. We also discuss compressed and uncompressed modes available for data formats like HDF5 and netCDF. When disregarding precision, the best data format from the size perspective is lossy HDF5 without compression. Losless HDF5 without compression show the best speed performance. Lossy HDF5 without compression is the best balance between size reduction and speed. However, for specific criteria and types of files, there might be better candidates as detailed in the conclusion.

Deoxyribonucleic acid (DNA) is emerging as an alternative archival memory technology. Recent advancements in DNA synthesis and sequencing have both increased the capacity and decreased the cost of storing information in de novo synthesized DNA pools. In this survey, we review methods for translating digital data to and/or from DNA molecules. An emphasis is placed on methods which have been validated by storing and retrieving real-world data via in-vitro experiments. [ABSTRACT FROM AUTHOR]

The development of smart water in drainage systems can achieve real-time monitoring, analysis and control from rainfall to urban rivers, water-users and sewage-treatment plants. Relying on artificial intelligence, smart water can achieve inundation control. This article summarizes the recent advances of smart water in China from three aspects: data collection, data storage and data analysis, seeking to understand the current development from the important points of smart water technology. At the end, we point out the challenges in the development of the smart water drainage system, and provide the perspectives for the future development of smart water. [ABSTRACT FROM AUTHOR]

A robust and reliable detection scheme of RNA tails grown on a double-stranded DNA (dsDNA) can pave the way to a denser encoding of DNA-based storage systems that use the punch card mechanism. Here, we develop a systematic algorithmic approach based on signal processing to detect the presence of RNA tails on dsDNA as well as to differentiate the tail lengths from the transverse conductance signal of MoS2 membrane nanopores. Combining all-atom molecular dynamics simulations with electronic transport modeling, we suggest a method to detect RNA tails with lengths of 10, 15, and 20 nucleotides separated by 10 base pairs. Modified dwell times obtained by using normalized DNA velocity provide an easy and intuitive way to distinguish the lengths of the RNA tails. We show that this technique can be extended for multiple tails, separation distances, and substrate DNA lengths. [ABSTRACT FROM AUTHOR]

The blockchain technology has provided a revolutionary way of secure data storage and transfer. It is a distributed ledger technology (DLT) that creates a secure and immutable record of transactions. This technology has been used to create secure and reliable systems for financial institutions. The blockchain based security system provides a secure platform for financial institutions. It ensures that the transactions are secure and immutable. This technology has been used to create digital assets that can be securely stored and transferred. It also ensures that the transactions are transparent and secure. The blockchain technology can be used to enable secure transactions between financial institutions. By using the blockchain, financial institutions can create a secure platform for transactions. This will ensure that the transactions are secure and immutable. It also ensures that the transactions are transparent and secure. The blockchain technology can also be used to create a secure system for digital identity management. This will ensure that the digital identities of the users are secure and immutable. It also ensures that the users' identity is protected and secure. The blockchain technology can also be used to create a secure platform for digital payments. This will ensure that the payments are secure and immutable. It also ensures that the payments are transparent and secure. [ABSTRACT FROM AUTHOR]

The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we performa comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this area. In particular, we divide intelligent identification over power big data into data acquisition and storage processes, anomaly detection and fault discrimination processes, and fault tracing for dispatching operations during communication. A detailed survey of the solutions to the challenges in intelligent identification over power big data is then presented. Moreover, opportunities and future directions are outlined. [ABSTRACT FROM AUTHOR]

A summary of the numerous hybrid machine learning (HML) patterns is provided in this paper, which covers the complete ML lifecycle from model construction to data preparation to training to deployment to ongoing management. As a resource for the primary decision and control of production systems, industrial information systems (IIS) is a major research field in industrial systems management. Industrial and manufacturing methods are being inundated with massive amounts of data due to the increasing use of industrial information systems (IIS). Data management in networked industrial systems is examined in this paper. We recommend hybrid machine learning (HML) patterns for these customers as a stop-gap measure on the road to the cloud. To overcome the missing data problem, we propose using hybrid machine learning (HML) to solve this issue. This challenge has been given a more comprehensive range of possible solutions thanks to advances in machine learning technology. Here, a complex industrial information system based on a hybrid machine learning model (CIIS-HMLM) is proposed to address recovering the sensor’s lost data that failed. Nonlinear data modeling using an intelligent algorithm is discussed in detail. In addition, this presents a method for processing data to ensure uninterrupted service for consumers using HML. We classify many research difficulties related to the effective design and proper implementation of CIIS-HMLM. As a wrap-up, we provide a few ideas for further research on this topic. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, the development of blockchain technology has also been deeply impacted. When performing block verification in the blockchain network, if all transactions are verified on the chain, this will cause the accumulation of data on the chain, resulting in data storage problems. At the same time, the security of data is also challenged, which will put enormous pressure on the block, resulting in extremely low communication efficiency of the block. The traditional blockchain system uses the Merkle Tree method to store data. While verifying the integrity and correctness of the data, the amount of proof is large, and it is impossible to verify the data in batches. A large amount of data proof will greatly impact the verification efficiency, which will cause end-to-end communication delays and seriously affect the blockchain system’s stability, efficiency, and security. In order to solve this problem, this paper proposes to replace the Merkle tree with polynomial commitments, which take advantage of the properties of polynomials to reduce the proof size and communication consumption. By realizing the ingenious use of aggregated proof and smart contracts, the verification efficiency of blocks is improved, and the pressure of node communication is reduced. [ABSTRACT FROM AUTHOR]

The exponential growth of data necessitates an effective data storage scheme, which helps to effectively manage the large quantity of data. To accomplish this, Deoxyribonucleic Acid (DNA) digital data storage process can be employed, which encodes and decodes binary data to and from synthesized strands of DNA. Vector quantization (VQ) is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency. This article introduces a new DNA Computing with Water Strider Algorithm based Vector Quantization (DNAC-WSAVQ) technique for Data Storage Systems. The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage. Besides, the DNAC-WSAVQ model initially performs DNA encoding on the input images to generate a binary encoded form. In addition, a Water Strider algorithm with Linde-Buzo-Gray (WSA-LBG) model is applied for the compression process and thereby storage area can be considerably minimized. In order to generate optimal codebook for LBG, the WSA is applied to it. The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures. The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods. [ABSTRACT FROM AUTHOR]

The emerging field of environmental DNA (eDNA) research lacks universal guidelines for ensuring data produced are FAIR–findable, accessible, interoperable, and reusable– despite growing awareness of the importance of such practices. In order to better understand these data usability challenges, we systematically reviewed 60 peer reviewed articles conducting a specific subset of eDNA research: metabarcoding studies in marine environments. For each article, we characterized approximately 90 features across several categories: general article attributes and topics, methodological choices, types of metadata included, and availability and storage of sequence data. Analyzing these characteristics, we identified several barriers to data accessibility, including a lack of common context and vocabulary across the articles, missing metadata, supplementary information limitations, and a concentration of both sample collection and analysis in the United States. While some of these barriers require significant effort to address, we also found many instances where small choices made by authors and journals could have an outsized influence on the discoverability and reusability of data. Promisingly, articles also showed consistency and creativity in data storage choices as well as a strong trend toward open access publishing. Our analysis underscores the need to think critically about data accessibility and usability as marine eDNA metabarcoding studies, and eDNA projects more broadly, continue to proliferate. [ABSTRACT FROM AUTHOR]

Since the last decade, cloud-based electronic health records (EHRs) have gained significant attention to enable remote patient monitoring. The recent development of Healthcare 4.0 using the Internet of Things (IoT) components and cloud computing to access medical operations remotely has gained the researcher's attention from a smart city perspective. Healthcare 4.0 mainly consisted of periodic medical data sensing, aggregation, data transmission, data sharing, and data storage. The sensitive and personal data of patients lead to several challenges while protecting it from hackers. Therefore storing, accessing, and sharing the patient medical information on the cloud needs security attention that data should not be compromised by the authorized user's components of E-healthcare systems. To achieve secure medical data storage, sharing, and accessing in cloud service provider, several cryptography algorithms are designed so far. However, such conventional solutions failed to achieve the trade-off between the requirements of EHR security solutions such as computational efficiency, service side verification, user side verifications, without the trusted third party, and strong security. Blockchain-based security solutions gained significant attention in the recent past due to the ability to provide strong security for data storage and sharing with the minimum computation efforts. The blockchain made focused on bitcoin technology among the researchers. Utilizing the blockchain which secure healthcare records management has been of recent interest. This paper presents the systematic study of modern blockchain-based solutions for securing medical data with or without cloud computing. We implement and evaluate the different methods using blockchain in this paper. According to the research studies, the research gaps, challenges, and future roadmap are the outcomes of this paper that boost emerging Healthcare 4.0 technology. [ABSTRACT FROM AUTHOR]

The efficiency of a data mining process depends on the data structure used to find frequent itemsets. Two approaches are possible: use the original transaction dataset or transform it into another more compact structure. Many algorithms use trees as compact structure, like FP-Tree and the associated algorithm FP-Growth. Although this structure reduces the number of scans (only 2), its efficiency depends on two criteria: (i) the size of the support (small or large); (ii) the type of transaction dataset (sparse or dense). But these two criteria can generate very large trees. In this paper, we propose a new tree-based structure that emphasizes on transactions and not on itemsets. Hence, we avoid the problem of support values that have a negative impact on the generated tree. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]