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Big data has posed problems for businesses, the Information Technology (IT) sector and the science community. The problems posed by big data can be effectively addressed using cloud computing and associated distributed computing technology. Cloud computing and big data are two significant past-year problems that allow high-efficiency and competitive computing tools to be delivered as IT services. The paper aims to examine the role of the cloud as a tool for managing big data in various aspects to help businesses.

This paper delivers solutions in the cloud for storing, compressing, analyzing and processing big data. Hence, articles were divided into four categories: articles on big data storage, articles on big data processing, articles on analyzing and finally, articles on data compression in cloud computing. This article is based on a systematic literature review. Also, it is based on a review of 19 published papers on big data.

From the results, it can be inferred that cloud computing technology has features that can be useful for big data management. Challenging issues are raised in each section. For example, in storing big data, privacy and security issues are challenging.

There were limitations to this systematic review. The first limitation is that only English articles were reviewed. Also, articles that matched the keywords were used. Finally, in this review, authoritative articles were reviewed, and slides and tutorials were avoided.

The research presents new insight into the business value of cloud computing in interfirm collaborations.

Previous research has often examined other aspects of big data in the cloud. This article takes a new approach to the subject. It allows big data researchers to comprehend the various aspects of big data management in the cloud. In addition, setting an agenda for future research saves time and effort for readers searching for topics within big data.

The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.

This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.

For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.

The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

For some time past satellites have been orbiting the Earth and sending back images of it to ground stations. These remote‐sensing images are now accumulating in large numbers: present plans for satellite launches indicate that the growth rate will continue to rise further into the 1990s. In consequence, satellite remote‐sensing data are raising urgent questions concerning their efficient storage and rapid retrieval. The experience of handling of remote‐sensing images is likely to influence future developments in computer graphics information more generally. We review here the current position regarding these problems and the steps being taken to overcome them.

This chapter provides an integrated overview of digital implementation in smart city ecosystems, focusing on the data ecosystem, the data value chain, and the digital implementation in smart cities. It acknowledges the opportunities and challenges presented by the growth of big data and its impact on delivering public services and coordinating local ecosystems in smart cities. This chapter introduces the concept of a smart city data ecosystem and explores the implementation of digital solutions, highlighting the role of local management and internal and external providers. It examines the digitalization of processes and services, technological infrastructure, and social media in the context of the smart city, emphasizing the interaction between local and national data ecosystems. This chapter incorporates interviews with smart city managers, policymakers, and technicians involved in the digital development process, offering valuable insights into the practical implementation and usage of data management within smart cities and the potential impact on urban development.

The aim of this research is to secure the sensitive outsourced data with minimum encryption within the cloud provider. Unfaithful solutions for providing privacy and security along with performance issues by encryption usage of outsourced data are the main motivation points of this research.

This paper presents a method for secure and confidential storage of data in the cloud environment based on fragmentation. The method supports minimal encryption to minimize the computations overhead due to encryption. The proposed method uses normalization of relational databases, tables are categorized based on user requirements relating to performance, availability and serviceability, and exported to XML as fragments. After defining the fragments and assigning the appropriate confidentiality levels, the lowest number of Cloud Service Providers (CSPs) is used required to store all fragments that must remain unlinkable in separate locations.

Particularly in the cloud databases are sometimes de‐normalised (their normal form is decreased to lower level) to increase the performance.

The paper proposes a methodology to minimize the need for encryption and instead focus on making data entities unlinkable so that even in the case of a security breach for one set of data, the privacy impact on the whole is limited. The paper would be relevant to those people whose main concern is to preserve data privacy in distributed systems.

The graphical representation of a finite element model (undirected graphs) imposes some constraints on the choice of storage techniques and data structures; first, the storage structure must deal efficiently with sparse matrices; second, the retrieval method of an edge, of a finite element model, around selected nodes must minimize the multiple occurrences of the same edge if plotting efficiency is to be achieved; and third, the insertion and extraction of edges in a data structure must be independent of the selected nodes identification scheme. This paper evaluates the relative merit of elementary storage methods and data structures in terms of the time and space costs required to satisfy the above constraints. The theoretical costs are derived and the experimental costs are evaluated and compared. Depending on the homogeneity of the degree of the nodes, a static data structure or a linked list data structure using listed or sectioned hashing techniques are shown to yield the minimum time and space costs.

In order to compliment the growing use of renewable energies in the US, additional technologies must be employed on the bulk power system. This paper aims to forecast the most probable energy storage technologies.

The methodology was deployed in two steps: evaluate the potential energy storage technologies that could complement a wind turbine or photovoltaic system; and forecast which of these technologies is best poised to become a viable solution to the energy storage problem facing these renewable technologies.

Based on the publication and patent data, compressed air energy is set to be the fastest growing complimentary technology to wind energy. Two of these types of plants are currently in existence today as mentioned previously indicating the technology is commercially available. This technology has great potential; however, implementing this technology involves finding or creating underground airtight caverns in usable locations.

The number of variables have been limited due to the methodologies chosen for this analysis. The research can be expanded using other criteria such as cost, cost of capital, economies of scale, environmental concerns, social and political constraints.

This paper provides an assessment that was indicated as necessary by those who identified the need for the development of energy storage technologies for future electricity generation.

Technical advantages embraced by blockchain, such as distributed ledger, P2P networks, consensus mechanisms and smart contracts, are highly compatible with addressing the security issues of transferring and storing judicial documents and obtaining the feedback and evaluation of judicial translation services in cases with foreign elements. Therefore, based on this, a consortium blockchain-based model for supervising the overall process of judicial translation services in cases with foreign elements is proposed.

Some judicial documents are required to be translated when there are language barriers in cases with foreign elements. The purpose of this paper is expected to address security issues, which is ignored, in the process of translating judicial documents.

The experimental results show that the model constructed in this paper can effectively guarantee the security and privacy of transferring and storing translated judicial documents in cases with foreign elements, and realize the credibility and traceability of feedbacks and evaluations of judicial translation services. In addition, the underlying network communications is stable and the speed for processing data can meet the requirements of practical application.

The research in this paper provides an innovative scheme for judicial translation services in cases with foreign elements. The model constructed is conducive to protecting the security of the transfer and storage of judicial documents and improving the efficiency and modernization ability of hearing cases with foreign elements.

To obtain first‐hand data on the main challenges in preserving digital resources in libraries, archives and information centres in China.

The data in this paper have been acquired by e‐mail questionnaire. The conclusions are based on feedback from 57 respondents, distributed in 14 provinces in China, who work in libraries, archives and information centres with rich digital collections and long‐term experience in preserving digital resources.

The authors consider that the long‐term preservation of digital resources in China faces two critical difficulties: firstly, the pressure on preserving digital resources has not been fully recognised and so the relevant training in the management and operation of digital preservation is absent; secondly, government departments attach little importance to this field and lack uniform guidelines. Therefore, the paper suggests that relevant international conferences or workshops should be held in China, and that a working group led by the National Development and Reform Commission of China should be set up to plan the construction, sharing and maintenance of Chinese digital resources.

This research is limited to Chinese administration systems. Although the data in this paper are authentic and representative, nevertheless it is a small sample of the numerous document collection organisations in the country.

The data and analysis in this paper should greatly help international colleagues understand the status regarding the preservation of digital resources in China.

This research is the first of its kind undertaken in China and will provide a new vision for international peers and organisations involved in digital documentary heritage around the world.

A survey of current work on database systems is presented. The area is divided into three main sectors: data models, data languages and support for database operations. Data models are presented as the link between the database and the real world. Languages range from formal algebraic languages to attempts to use a dialogue in English to formulate queries. The support includes hardware for content addressing, database machines and software techniques for optimizing and evaluating group expressions. Mathematical models are used to organize this support. Throughout there is a tutorial component and evaluation, which in both cases is related to the application of database ideas to documentation.