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Increasingly, the content that libraries collect is no longer on paper, a long-lived, medium whose technology changes very slowly and with which they have centuries of experience. Instead, it is stored on relatively short-lived digital media whose technology appears to change rapidly and with which they have little history. The paper aims to discuss this issue.

The storage media industry is highly competitive and is currently evolving rapidly as flash, a solid state medium, displaces spinning disk from many applications. Long-term archival storage is a small part of the total storage market. It typically re-uses media and systems intended for more general bulk storage.

What are the medium-term prospects for change in this market?

Much of this material has appeared in blog posts and talks aimed at storage experts, such as the recent DARPA workshop on future of storage. It is presented here for a librarian audience with the necessary additional exposition and background.

The purpose of this paper is to investigate the prospects of current storage technologies for long-term preservation of big data in digital libraries.

The study employs a systematic and critical review of the relevant literature to explore the prospects of current storage technologies for long-term preservation of big data in digital libraries. Online computer databases were searched to identify the relevant literature published between 2000 and 2016. A specific inclusion and exclusion criterion was formulated and applied in two distinct rounds to determine the most relevant papers.

The study concludes that the current storage technologies are not viable for long-term preservation of big data in digital libraries. They can neither fulfil all the storage demands nor alleviate the financial expenditures of digital libraries. The study also points out that migrating to emerging storage technologies in digital libraries is a long-term viable solution.

The study suggests that continuous innovation and research efforts in current storage technologies are required to lessen the impact of storage shortage on digital libraries, and to allow emerging storage technologies to advance further and take over. At the same time, more aggressive research and development efforts are required by academics and industry to further advance the emerging storage technologies for their timely and swift adoption by digital libraries.

The study reveals that digital libraries, besides incurring significant financial expenditures, will suffer from potential loss of information due to storage shortage for long-term preservation of big data, if current storage technologies are employed by them. Therefore, policy makers and practitioners should meticulously choose storage technologies for long-term preservation of big data in digital libraries.

This type of holistic study that investigates the prospects of magnetic drive technology, solid-state drive technology, and data-reduction techniques for long-term preservation of big data in digital libraries has not been conducted in the field previously, and so provides a novel contribution. The study arms academics, practitioners, policy makers, and industry with the deep understanding of the problem, technical details to choose storage technologies meticulously, greater insight to frame sustainable policies, and opportunities to address various research problems.

The purpose of this paper is to use cloud storage in digital preservation by analyzing the pricing and data retrieval models. The author recommends strategies to minimize the costs and believes cloud storage is worthy of serious consideration.

Few articles have been published to show the uses of cloud storage in libraries. The cost is the main concern. An overview of cloud storage pricing shows a price drop once every one or one-and-a-half years. The author emphasize the data transfer-out costs and demonstrate a case study. Comparisons and analysis of S3 and Glacier have been conducted to show the differences in retrieval and costs.

Cloud storage solutions like Glacier can be very attractive for long-term digital preservation if data can be operated within the provider’s same data zone and data transfer-out can be minimized.

Institutions can benefit from cloud storage by understanding the cost models and data retrieval models. Multiple strategies are suggested to minimize the costs.

The paper is intended to bridge the gap of uses of cloud storage. Cloud storage pricing especially data transfer-out pricing charts are presented to show the price drops over the past eight years. Costs and analysis of storing and retrieving data in Amazon S3 and Glacier are discussed in details. Comparisons of S3 and Glacier show that Glacier has uniqueness and advantages over other cloud storage solutions. Finally strategies are suggested to minimize the costs of using cloud storage. The analysis shows that cloud storage can be very useful in digital preservation.

The main purpose of this study is to present an overview of the state of the art of the RFID technology in terms of data storage approaches in construction cases, and to identify the factors that require different approaches to data storage (e.g. on tags or on a remote database) in RFID applications in the construction industry.

A literature survey was conducted and the contexts of 37 construction industry cases were investigated to determine the factors that affect the decision of data storage approach and the types of information groups that were stored in each case. Additionally, 79 cases were reviewed from other industries to provide insights.

The literature review showed that, many cases in the construction industry preferred storing additional data on RFID tags such as identification, technical and historical information. The factors affecting the selection of data storage approach in RFID applications were identified: application environment, cost efficiency, multiple number of parties, need for monitoring up‐to‐date progress data, collecting environmental conditions, in situ (on‐board) data storage, industry‐related specifications, and reading range requirement.

The high proportion of cases which stored data on tags demonstrate that there is a need for tags/storage media that are specially designed for the construction industry because most tags currently have either minimum or limited memories.

The analysis of the investigated cases and the factors that were identified to be affecting the data storage approach decision making can assist construction practitioners and owners in selecting an appropriate data storage approach for their projects.

Although cloud storage services can bring users valuable convenience, they can be technically complex and intrinsically insecure. Therefore, this research explores the concerns of academic users regarding cloud security and technical issues and how such problems may influence their continuous use in daily life.

This qualitative study used a semi-structured interview approach comprising six main open-ended questions to explore the information security and technical issues for the continuous use of cloud storage services by 20 undergraduate students in Hong Kong.

The analysis revealed cloud storage service users' major security and technical concerns, particularly synchronization and backup issues, were the most significant technical barrier to the continuing personal use of cloud storage services.

Existing literature has focused on how cloud computing services could bring benefits and security and privacy-related risks to organizations rather than security and technical issues of personal use, especially in the Asian academic context.

Data‐centric storage is an efficient scheme to store and retrieve event data in sensor networks, but with the multi‐hop routing nature of sensor networks, the communication cost of the home nodes and their neighboring nodes tends to be much higher than the other nodes. These hot‐spots can adversely impact system lifetime by draining off their limited energy rapidly. In this paper, we present Zone‐Repartitioning, a load‐balancing mechanism that reduces the energy consumption of the hot‐spots by distributing their communication load while event frequency is high. The trade‐off between event storage cost and query cost makes Zone Repartitioning a competitive approach in different kinds of applications. We compare the performance of Zone Repartitioning against GHT and show that Zone Repartitioning provides better adaptability in various sensor network scenarios.

Until now, a lot of research has been done and applied to provide security and original data from one user to another, such as third-party auditing and several schemes for securing the data, such as the generation of the key with the help of encryption algorithms like Rivest–Shamir–Adleman and others. Here are some of the related works that have been done previously. Remote damage control resuscitation (RDCR) scheme by Yan et al. (2017) is proposed based on the minimum bandwidth. By enabling the third party to perform the verification of public integrity. Although it supports the repair management for the corrupt data and tries to recover the original data, in practicality it fails to do so, and thus it takes more computation and communication cost than our proposed system. In a paper by Chen et al. (2015), using broadcast encryption, an idea for cloud storage data sharing has been developed. This technique aims to accomplish both broadcast data and dynamic sharing, allowing users to join and leave a group without affecting the electronic press kit (EPK). In this case, the theoretical notion was true and new, but the system’s practicality and efficiency were not acceptable, and the system’s security was also jeopardised because it proposed adding a member without altering any keys. In this research, an identity-based encryption strategy for data sharing was investigated, as well as key management and metadata techniques to improve model security (Jiang and Guo, 2017). The forward and reverse ciphertext security is supplied here. However, it is more difficult to put into practice, and one of its limitations is that it can only be used for very large amounts of cloud storage. Here, it extends support for dynamic data modification by batch auditing. The important feature of the secure and efficient privacy preserving provable data possession in cloud storage scheme was to support every important feature which includes data dynamics, privacy preservation, batch auditing and blockers verification for an untrusted and an outsourced storage model (Pathare and Chouragadec, 2017). A homomorphic signature mechanism was devised to prevent the usage of the public key certificate, which was based on the new id. This signature system was shown to be resistant to the id attack on the random oracle model and the assault of forged message (Nayak and Tripathy, 2018; Lin et al., 2017). When storing data in a public cloud, one issue is that the data owner must give an enormous number of keys to the users in order for them to access the files. At this place, the knowledge assisted software engineering (KASE) plan was publicly unveiled for the first time. While sharing a huge number of documents, the data owner simply has to supply the specific key to the user, and the user only needs to provide the single trapdoor. Although the concept is innovative, the KASE technique does not apply to the increasingly common manufactured cloud. Cui et al. (2016) claim that as the amount of data grows, distribution management system (DMS) will be unable to handle it. As a result, various proven data possession (PDP) schemes have been developed, and practically all data lacks security. So, here in these certificates, PDP was introduced, which was based on bilinear pairing. Because of its feature of being robust as well as efficient, this is mostly applicable in DMS. The main purpose of this research is to design and implement a secure cloud infrastructure for sharing group data. This research provides an efficient and secure protocol for multiple user data in the cloud, allowing many users to easily share data.

The methodology and contribution of this paper is given as follows. The major goal of this study is to design and implement a secure cloud infrastructure for sharing group data. This study provides an efficient and secure protocol for multiple user data in cloud, allowing several users to share data without difficulty. The primary purpose of this research is to design and implement a secure cloud infrastructure for sharing group data. This research develops an efficient and secure protocol for multiple user data in the cloud, allowing numerous users to exchange data without difficulty. Selection scheme design (SSD) comprises two algorithms; first algorithm is designed for limited users and algorithm 2 is redesigned for the multiple users. Further, the authors design SSD-security protocol which comprises a three-phase model, namely, Phase 1, Phase 2 and Phase 3. Phase 1 generates the parameters and distributes the private key, the second phase generates the general key for all the users that are available and third phase is designed to prevent the dishonest user to entertain in data sharing.

Data sharing in cloud computing provides unlimited computational resources and storage to enterprise and individuals; moreover, cloud computing leads to several privacy and security concerns such as fault tolerance, reliability, confidentiality and data integrity. Furthermore, the key consensus mechanism is fundamental cryptographic primitive for secure communication; moreover, motivated by this phenomenon, the authors developed SSDmechanismwhich embraces the multiple users in the data-sharing model.

Files shared in the cloud should be encrypted for security purpose; later these files are decrypted for the users to access the file. Furthermore, the key consensus process is a crucial cryptographic primitive for secure communication; additionally, the authors devised the SSD mechanism, which incorporates numerous users in the data-sharing model, as a result of this phenomena. For evaluation of the SSD method, the authors have considered the ideal environment of the system, that is, the authors have used java as a programming language and eclipse as the integrated drive electronics tool for the proposed model evaluation. Hardware configuration of the model is such that it is packed with 4 GB RAM and i7 processor, the authors have used the PBC library for the pairing operations (PBC Library, 2022). Furthermore, in the following section of this paper, the number of users is varied to compare with the existing methodology RDIC (Li et al., 2020). For the purposes of the SSD-security protocol, a prime number is chosen as the number of users in this work.

The purpose of this paper is to show the potential of self‐organized semantic storage services. The semantic web has provided a vision of how to build the applications of the future. A software component dedicated to the storage and retrieval of semantic information is an important but generic part of these applications. Apart from mere functionality, these storage components also have to provide good performance regarding the non‐functional requirements scalability, adaptability and robustness. Distributing the task of storing and querying semantic information onto multiple computers is a way of achieving this performance. However, the distribution of a task onto a set of computers connected using a communication network is not trivial. One solution is self‐organized technologies, where no central entity coordinates the system's operation.

Based on the available literature on large‐scale semantic storage systems, the paper analyzes the underlying distribution algorithm, with special focus on the properties of semantic information and corresponding queries. The paper compares the approaches and identify their shortcomings.

All analyzed approaches and their underlying technologies were unable to distribute large amounts of semantic information and queries in a generic way while still being able to react on changing network infrastructure. Nonetheless, as each concept represented a unique trade‐off between these goals, the paper points out how self‐organization is crucial to perform well at least in a subset of them.

The contribution of this paper is a literature review aimed at showing the potential of self‐organized semantic storage services. A case is made for self‐organization in a distributed storage system as the key to excellence in the relevant non‐functional requirements: scalability, adaptability and robustness.

The purpose of this paper is to explore the challenges posed by Big Data to current trends in computation, networking and storage technology at various stages of Big Data analysis. The work aims to bridge the gap between theory and practice, and highlight the areas of potential research.

The study employs a systematic and critical review of the relevant literature to explore the challenges posed by Big Data to hardware technology, and assess the worthiness of hardware technology at various stages of Big Data analysis. Online computer-databases were searched to identify the literature relevant to: Big Data requirements and challenges; and evolution and current trends of hardware technology.

The findings reveal that even though current hardware technology has not evolved with the motivation to support Big Data analysis, it significantly supports Big Data analysis at all stages. However, they also point toward some important shortcomings and challenges of current technology trends. These include: lack of intelligent Big Data sources; need for scalable real-time analysis capability; lack of support (in networks) for latency-bound applications; need for necessary augmentation (in network support) for peer-to-peer networks; and rethinking on cost-effective high-performance storage subsystem.

The study suggests that a lot of research is yet to be done in hardware technology, if full potential of Big Data is to be unlocked.

The study suggests that practitioners need to meticulously choose the hardware infrastructure for Big Data considering the limitations of technology.

This research arms industry, enterprises and organizations with the concise and comprehensive technical-knowledge about the capability of current hardware technology trends in solving Big Data problems. It also highlights the areas of potential research and immediate attention which researchers can exploit to explore new ideas and existing practices.