Data Storage Management Research Articles

Research on Development and Application of Intelligent Cluster Management Platform for Shield Machine

Aiming at the problems of data acquisition, data storage, and equipment management that are caused by the scattered positions of shield machines during the simultaneous construction of multiple projects, an intelligent cluster management platform for shield machines is designed. Focusing on the key technologies of platform building and application from the aspects of overall platform architecture, data acquisition, data transmission, data storage, this paper proposes an intelligent cluster management platform, which integrates the functions of multi-project centralized monitoring, multi-terminal access and collaborative management. This paper provides guidance for the building of similar cluster management platform from the aspects of technology development, system application and comprehensive management, improving the construction efficiency and intelligent management level for shield machines.

Research on Educational Information Platform Based on Cloud Computing

The traditional method only pays attention to hardware construction and ignores the data processing steps, which leads to high redundant resource occupancy rate, untimely resource sharing, and low platform data safety factor. In order to solve this problem, this paper establishes an educational information platform based on cloud computing. The platform gives the overall structure of the education information platform, including the business layer and the support layer. Then the external interface is designed. Based on the MySQL database interface, users are allowed to use custom data formats and storage management modes to ensure flexibility in using data resources and improve the compatibility between smart terminals and virtual machines through the remote desktop terminal architecture. Through the educational data compression and educational resource sharing model, the generation of redundant messages is reduced, thereby realizing the design of the educational information platform. Experimental results show that this method can effectively reduce the occupancy rate of redundant resources, save network bandwidth, and improve the data safety factor of the platform. The resource sharing time is always less than 2.0 s, which verifies the effectiveness of the method

Digital and Decentralized Management of Patient Data in Healthcare Using Blockchain Implementations

Blockchain solutions offer efficient approaches for trustworthy data management, especially in the medical field when storing and processing sensitive patient data. Many institutional and industrial facilities have already recognized the importance of the technology for the health sector and have also formulated basic ideas, concepts and main use cases, but concrete implementations and executions are comparatively rare. This mini review examines current research on specific blockchain implementations in healthcare that go beyond the state of concept studies or theoretical implementation ideas and describes the most promising systems based on systematic literature research. The review shows that secure storage and easy access to complete patient data is becoming increasingly important. Blockchain technology can be used as a secure, transparent and digital way to meet these needs. Hybrid solutions consisting of conventional data storage and blockchain-based access management are increasingly being developed and implemented. The automation of blockchain processes through smart contracts is also recommended. The review further reveals ambiguities in the use of permissioned and permissionless blockchain frameworks, machine learning (ML) integration as well as the question of which data should be stored in the blockchain and how this should be viewed legally. Therefore, there is still a need for further research, especially on these aspects, in order to further establish the use of blockchains in healthcare.

Understanding African-American Women's Low-sodium Dietary Practices: A Qualitative Perspective.

Objectives: This qualitative study explored how African-American women perceived certain typical low-sodium dietary practices. Methods: This research applied a single category focus group design; 22 African-American women (aged 18 years and older) having hypertension completed a demographic survey before focus group sessions. Constant comparison analysis helped to identify major themes. NVivo 11 aided in data storage, organization, and information management. Results: We found that individuals tend to take action if they believe the action will address their health condition. Some participants indicated they were taking measures to reduce their sodium intake. However, other participants were not actively engaging in reducing their sodium intake despite acknowledging the positive benefits of doing so. Conclusions: Study findings showed the participants perceived the potential benefits of dietary changes as being less significant than the perceived barriers to implementing them. Furthermore, these perceived barriers to taking action may have stemmed from these women's lack of motivation and self-efficacy.

Analysis of Heterogeneous Data Storage and Access Control Management for Cloud Computing Under M/M/c Queueing Model

This study focuses on challenges faced during optimization of the performance of multiple users under encrypted data storage. Traditional single storage instance schemes are much concerned about the application scenarios rather than data sensitivity. This paper provides an optimization function for analyzing the performance parameters of developed queueing system. On the other hand, simulation results are further examined in order to optimize the average waiting time, average queue length, and total number of data packets. In addition, deduplication check provides more storage by eliminating duplicate files, and the remaining files further get encrypted by two cryptographic algorithms, namely advanced encryption standard (AES) and Rivest–Shamir–Adleman (RSA), respectively. Moreover, this paper emphasises performance of data in terms of storage and security; on the same basis, its comparison and implementation has been done. At last, conclusion is given.

Development and psychometric evaluation of the cloud computing acceptance questionnaire for academic libraries

Cloud computing is a new technology that drives libraries to create and share knowledge, leading to the cost reduction, easy management, and unlimited data storage capacity. Given the importance of cloud computing technology and the lack of tools to investigate the factors affecting cloud computing acceptance in academic libraries; therefore, the aim of present study was to perform the development and psychometric evaluation of the cloud computing technology acceptance questionnaire for academic libraries. This study is a methodological research. After defining the concept of cloud computing acceptance, the initial version of the questionnaire consisting of 43 items and 11 components was designed. Then, face and content validity were used to assess validity, and Cronbach's alpha coefficient and intra-class correlation coefficient (ICC) were used to determine reliability. The results showed that the questionnaire has acceptable face validity (more than 1.5) and content validity (0.79). The Cronbach's alpha coefficient and the intra-class correlation coefficient were 0.77 and 0.92, respectively. In this study, for the first time, a tool was designed to evaluate the acceptance of cloud computing technology in academic libraries. Due to the fact that this tool has acceptable validity and reliability, it can be used in various studies and surveys.

A Hybrid Blockchain Model for Trusted Data of Supply Chain Finance.

Supply chain finance is an effective way to solve the problem of capital turnover of construction enterprises and stabilize economic growth under epidemic situation. Blockchain technology can solve the problems in the current supply chain finance business, such as incorrect information between banks and enterprises, lack of visibility in transaction process, and possible joint fraud in the core enterprise model. However, it still has problems such as inconvenient information verification, data fraud, and difficulty to achieve a balance between efficiency, security and cost. This paper presents a hybrid chain model combining PANDA (a consensus algorithm based on public chain) and X-Alliance (a consensus algorithm based on alliance chain). Such proposed hybrid chain model can process the transaction of each account in parallel, asynchronize from other unrelated accounts in the network, provide more reliable data storage and authority management, and ensure the ownership of change tracking data, which has higher performance and lower protection cost while ensuring data security and privacy security. According to the data of network crawling, the experimental results show that the throughput can reach 1200tps when four nodes are deployed. The model serves for the supply chain financial data management of engineering projects, making trade authenticity review, risk assessment and credit transmission of core enterprises more efficient. On this basis, each participant can carry out analysis and give early warning of capital flow, timely analyze and calculate the authenticity of transaction background. The proposed method provides a reference for the normal operation of the project fund under the COVID-19 epidemic.

Asymmetric data storage management scheme to ensure the safety of big data in multi-cloud environments based on deep learning

Asymmetric data storage management scheme to ensure the safety of big data in multi-cloud environments based on deep learning

Exploration and Analysis of University Scientific Research Data Management Strategies under Big Data Environment

From the perspective of the current big data environment, briefly describe the urgency of university scientific research data management, and use this as a basis to discuss the university scientific research data management strategy under the big data environment, and propose to conduct sufficient research on scientific research data management and pay attention to scientific research data Strategies such as dynamic management, strengthening the storage and security management of scientific research data, and enhancing the value of scientific research data results through publication and citation.

Design of Information Acquisition System for High Voltage Pulse Power Supply

Capacitor discharge is the main application form of pulse power technology, which is simple in principle and easy to implement. The design of pulse power supply system requires the information management of a large amount of experimental data. While the conventional acquisition system only pays attention to the display of the power waveform, and lacks the effective management of the intermediate state information and discharge waveform, which cannot meet the requirement of power supply. Aiming at this need, an integrated power supply information acquisition system based on Labview and Access database is designed. This system takes Labview as the software development platform and Access as the background database to realize the acquisition, storage and information management of power data. In the program design, the producer-consumer mode is adopted for data collection and processing, and the queue technology is used for circular communication to ensure the integrity and reliability of data processing. The actual operation shows that the acquisition process of the system is stable and reliable, and the information management of the power data is realized.

A big data of rehabilitation process management based on block chain

This article uses block chain and big data technology to design a process management, and proposes a medical data sharing scheme based on block chain, which can effectively solve the problem that rehabilitation data may be tampered or deleted and improve work efficiency. Uses the chain block in the chain alliance technology, we design a rehabilitation information data storage management scheme, this scheme will be stored module is divided into two parts, one is the recovery operation data storage module, the module using relational data store inventory data, data read operations related to the rehabilitation treatment all can still read the database; The other is the audit log word storage module, which uses key-value database word storage to store the user’s operation contents of the business data, and any modification of the business data will be written to the data module. The blockchain-based rehabilitation big data process management model proposed in this article not only well meets the regulatory audit requirements of the rehabilitation treatment management system, but also forms a real alliance among the health administration departments, hospitals and recovered patients. Conclusions: Through rehabilitation big data sharing system, medical personnel through case information to rehabilitation plan provides more valuable reference, and carries on the reasonable use of these information, so as to enhance the level of rehabilitation treatment and effective information will be fully in the medical data mining, medical personnel can strengthen communication and cooperation with information technology personnel, so as to create more convenient, applicable platform for the rehabilitation.

WITHDRAWN: S2OPE security: Shuffle standard onetime padding encryption for improving secured data storage in decentralized cloud environment

Blockchain is one of the most promising and service resourceful technologies for cloud architecture security in development. Specifically for Check transactions in data storage, access control, and key management in a Distributed Database System (DDS). By the consequences, the Blockchain is very proactivefor access control by verified integrity proofing system when it comes to using encrypted data structures. Due to this access control probably affect by various attacks because of key leakage concerns by improper security. In this paper, we propose a shuffle standard onetime padding encryption (S 2 OPE) for improving secured data storage in a decentralized cloud environment to resolve the security problems. To strengthen the security level we have proposed a hierarchical security system which is more secure than any of the single level encryption type. Initially, we make a shuffle standard cryptography key policy with optimized Key Digital Signature Policy (KDSP), besides, one-time padding makes legitimate key for integrity proofing to improve the access control by which the cloud data can only be accessed by legitimate users. Our algorithm is fast and secure in both directions to base on owner policy to request access to the files. If some data is lost, there is a hierarchical possibility of decoding technique, the data is very difficult to decrypt to improve the integrity of the security.

INTELLIGENT STEERING DRILLING TECHNOLOGY METHOD BASED ON CLOUD BIG DATA

Steering drilling technology is one of the most important technologies in the global petroleum industry in the 21st century, and it is also a key component of horizontal drilling, the core technology of the American Shale Gas Revolution. At present, the main research goal of steering drilling is to increase the drilling speed, reduce the drilling time and risk, and intelligence is an important way to achieve this goal. The article analyzes the application of big data and artificial intelligence in the petroleum industry at home and abroad, establishes a cloud big data intelligent steering drilling method framework, proposes an artificial intelligence inversion method for logging while drilling parameters, and points out the way to realize the management of cloud big data and intelligent algorithms, and draw the following conclusions: (1)The intelligent guided drilling method based on cloud big data mainly includes the things perception layer, the big data storage layer and the cloud platform decision layer. The things perception layer realizes the collection and transmission of key information of the wellsite to the big data center. The big data storage center is mainly responsible for data storage and cloud management. The cloud platform decision layer relies on the massive data in the big data center to perform cloud ground software control, artificial intelligence decision-making, and cloud platform management. (2)Select six geophysical parameters such as SP, GR, DEN, AC, CNL, and RT, and use different Machine Learning algorithms to build models to realize the independent identification of formation lithology. The Decision Tree model and the Random Forest model have an accuracy of 0.81 and 0.89 respectively, forming a set of schemes that can quickly and automatically describe the classification of lithological characteristics. (3)The cloud platform management decision is mainly used to decode real-time upload data downhole, and obtain drilling trajectories and logging curves. The cloud artificial intelligence decision-making module performs intelligent inversion and prediction of stratum and drilling parameters, realizes intelligent correction of drilling trajectories and intelligent optimization of drilling parameters, and ensures the accuracy and speed of drilling of intelligent steering engineering.

Towards design and implementation of security and privacy framework for Internet of Medical Things (IoMT) by leveraging blockchain and IPFS technology

The Internet of Medical Things (IoMT) is the next frontier in the digital revolution and it leverages IoT in the healthcare domain. The underlying technology has changed the current healthcare system by collecting real-time data of patients and providing a patient motioning system. But IoMT also presents a big challenge for data storage management, security, and privacy due to cloud-based storage. Today, this large volume of IoMT generated medical data is stored in the centralized storage system. However, centralization of patient sensitive information leads to a single point of failure, privacy, and security concern. To address these issues, we propose a smart contracts enabled consortium blockchain network. We integrated interplanetary file systems (IPFS) cluster node where smart contracts are deployed at the initial stage for authentication of patient’s and medical devices, the same cluster layer is also proposed as a distributed data storage layer for device-generated data after authentication and these data are securely transmitted over the consortium blockchain. The IPFS cluster node ensures the security and authentication of the devices and it also provides secure storage management in IoMT enabled healthcare system. The consortium network enables the privacy of data owing to hash-based storage in a block of IoMT enabled healthcare network.

Intelligent service system for distributed construction engineering facilities based on Internet of Things

This paper studies the implementation of distributed building smart construction technology. We analyze the role of building information models, Internet of Things, and pervasive computing in smart construction. Based analysis, we use integrated ideas and integrated frameworks for integration construction in the full life cycle of distributed buildings, and research from the perspective of full life cycle on distributed architectural intelligence construction management concepts. Equipment modeling and data storage management mainly include the establishment of equipment models and the storage and management of equipment real-time data. We design the database table structure by analyzing personnel, equipment, virtual space and the relationship between them, and establish the corresponding E-R model. We research and investigate the types and operating characteristics of equipment in buildings, classify IoT equipment, model several commonly used building networking equipment, and build equipment model libraries. We analyze the business process and data flow of building networked equipment management, and conduct research on the application of equipment virtualization management to realize the intelligent and personalized management of IoT equipment. Taking three smart buildings based on building information models as examples for empirical analysis, the evaluation grades of three projects are obtained by calculating the correlation degree, and then the projects are sorted according to the evaluation grades and the best projects are selected.

Modified Data Storage and Replication Mechanism with Frequent Use-Case Based Indexing

The real time applications demands high speed and reliable data access from the remote database. An effective logical data management strategy that handles simultaneous connections with better performance negotiation is inevitable. This work considers an e-health care application that proposes MongoDB based modified indexing and performance tuning methods. To cope with certain high frequency use case and its performance mandates, a flexible and efficient logical data management may be preferred. By analysing the data dependency, data decomposition concerns and the performance requirements of the specific use case of the medical application, a logical schema may be customized on an ala-carte basis. This work focused on the flexible logical data modeling schemes and its performance factors of the NoSql DB. The efficiency of unstructured data base management in storing and retrieving the e-health care data was analysed with a web based tool. To enable faster data retrieval and query processing over the distributed nodes, a Spark based storage engine was built on top of the MongoDB based data storage management. With Spark tool, the database has been made distributed as master–slave structures with suitable data replication mechanisms. In such distributed database the fail-over also implemented with the suitable replication mechanism. This work considered MongoDB based flexible schema modeling and Spark based distributed computation with multiple chunks of data. The flexible data modeling scheme with MongoDB with the on-demand Spark based computation framework was proposed. To facilitate the eventual consistency, scalability aspects of the e-health care applications, use case based indexing was proposed. With the effective data management, faster query processing the horizontal scalability has been increased. The overall efficiency and scalability of the proposed logical data management approach was analysed. Through the simulation studies, the proposed approach has been claimed to boost the performance of the bigdata based application to a considerable extent.

High density genotype storage for plant breeding in the Chado schema of Breedbase.

Modern breeding programs routinely use genome-wide information for selecting individuals to advance. The large volumes of genotypic information required present a challenge for data storage and query efficiency. Major use cases require genotyping data to be linked with trait phenotyping data. In contrast to phenotyping data that are often stored in relational database schemas, next-generation genotyping data are traditionally stored in non-relational storage systems due to their extremely large scope. This study presents a novel data model implemented in Breedbase (https://breedbase.org/) for uniting relational phenotyping data and non-relational genotyping data within the open-source PostgreSQL database engine. Breedbase is an open-source, web-database designed to manage all of a breeder’s informatics needs: management of field experiments, phenotypic and genotypic data collection and storage, and statistical analyses. The genotyping data is stored in a PostgreSQL data-type known as binary JavaScript Object Notation (JSONb), where the JSON structures closely follow the Variant Call Format (VCF) data model. The Breedbase genotyping data model can handle different ploidy levels, structural variants, and any genotype encoded in VCF. JSONb is both compressed and indexed, resulting in a space and time efficient system. Furthermore, file caching maximizes data retrieval performance. Integration of all breeding data within the Chado database schema retains referential integrity that may be lost when genotyping and phenotyping data are stored in separate systems. Benchmarking demonstrates that the system is fast enough for computation of a genomic relationship matrix (GRM) and genome wide association study (GWAS) for datasets involving 1,325 diploid Zea mays, 314 triploid Musa acuminata, and 924 diploid Manihot esculenta samples genotyped with 955,690, 142,119, and 287,952 genotype-by-sequencing (GBS) markers, respectively.

Computational Intelligence Enabled Cybersecurity for the Internet of Things

The computational intelligence (CI) based technologies play key roles in campaigning cybersecurity challenges in complex systems such as the Internet of Things (IoT), cyber-physical-systems (CPS), etc. The current IoT is facing increasingly security issues, such as vulnerabilities of IoT systems, malware detection, data security concerns, personal and public physical safety risk, privacy issues, data storage management following the exponential growth of IoT devices. This work aims at investigating the applicability of computational intelligence techniques in cybersecurity for IoT, including CI-enabled cybersecurity and privacy solutions, cyber defense technologies, intrusion detection techniques, and data security in IoT. This paper also attempts to provide new research directions and trends for the increasingly IoT security issues using computational intelligence technologies.

Intelligent interaction design research based on block chain communication technology and fuzzy system

With the increasing amount of information on the Internet, data storage management tends to be distributed. In distributed storage environment, users pay more and more attention to the timeliness of user interaction experience and the reliability of information interaction. However, the efficiency of users is often limited by the efficiency of data communication between distributed sites. One of the important goals of distributed data management is to improve the efficiency of data transmission and ensure the reliability of data transmission. Block chain technology is one of the emerging technologies supporting the development of management information system; it provides a solution for the storage, verification, transmission and communication of the distributed data. This paper focuses on solving the problem of block chain data transmission, and studies it from three aspects: improving the efficiency of data communication, ensuring the reliability of transmission, and improving the fairness of service, and different block chain data communication performance optimization strategies are proposed under the constraints of node communication capability, node trust, weight, priority of service request and other influencing factors.

A Reinforcement Learning Based Data Storage and Traffic Management in Information-Centric Data Center Networks

Data Center Networks (DCN), a core infrastructure of cloud computing, place heavy demands on efficient storage and management of massive data. The data storage scheme, which decides how to assign data to nodes for storage, has a significant impact on the performance of the data center. However, most of the existing solutions focus on where to store the data (i.e., the selection of storage node) but have not considered how to store them (i.e., the traffic management such as routing and transmission rate adjustment). By leveraging the Information-Centric Networks (ICN) architecture, this paper tackles the data storage and traffic management issue in Information-Centric Data Center Networks (ICDCN) based on Reinforcement Learning (RL) method, since RL has been developed as a promising solution to address dynamic network issues. We present a global optimization of joint traffic management and data storage and then solve it by the distributed multi-agent Q-learning. In ICDCN, the data is routed based on the data’s name, which achieves better routing scalability by decoupling the data and its physical location. Compared with IP’s stateless forwarding plane, the stateful forwarding information maintained at every node supports adaptively routing and hop-by-hop traffic control by using the Q-learning method. We evaluate our proposal on an NS-3-based simulator, and the results show that the proposed scheme can effectively reduce transmission time and increase throughput while achieving load-balanced among servers.