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Journal articles on the topic 'RDMS'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Cattaneo, Matias D., Rocío Titiunik, and Gonzalo Vazquez-Bare. "Analysis of regression-discontinuity designs with multiple cutoffs or multiple scores." Stata Journal: Promoting communications on statistics and Stata 20, no. 4 (December 2020): 866–91. http://dx.doi.org/10.1177/1536867x20976320.

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In this article, we introduce the Stata (and R) package rdmulti, which consists of three commands (rdmc, rdmcplot, rdms) for analyzing regression-discontinuity (RD) designs with multiple cutoffs or multiple scores. The command rdmc applies to noncumulative and cumulative multicutoff RD settings. It calculates pooled and cutoff-specific RD treatment effects and provides robust biascorrected inference procedures. Postestimation and inference is allowed. The command rdmcplot offers RD plots for multicutoff settings. Finally, the command rdms concerns multiscore settings, covering in particular cumulative cutoffs and two running variable contexts. It also calculates pooled and cutoff-specific RD treatment effects, provides robust bias-corrected inference procedures, and allows for postestimation and inference. These commands use the Stata (and R) package rdrobust for plotting, estimation, and inference. Companion R functions with the same syntax and capabilities are provided.

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2

Niu, Jinfang. "Diffusion and adoption of research data management services." Global Knowledge, Memory and Communication 69, no. 3 (September 6, 2019): 117–33. http://dx.doi.org/10.1108/gkmc-05-2019-0057.

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Purpose This paper aims to identify the diffusion patterns, especially the communication channels, in the diffusion and adoption of research data management services (RDMS) among libraries. Design/methodology/approach Literature about the RDMS in individual libraries was gathered and analyzed. Data relevant to the research questions were extracted and analyzed. Findings Early adopters conduct much original research to create RDMS and they often serve as change agents in diffusing their RDMS and related innovations to other libraries. In contrast, late adopters usually learn from early adopters and use their innovations for establishing their own RDMS. Communication channels used in diffusing RDMS deviate slightly from those reported in general diffusion of innovations (DOI) theories. Research limitations/implications Gathered literature provides incomplete and uneven information for RDMS adopters. This makes it difficult to identify adopter categories and test many generalizations in DOI theories. To overcome these limitations, surveys and interviews will be conducted in the future. Originality/value Findings from this project contribute to general DOI theories because RDMS is unique compared with many other innovations. The diffusion of RDMS is a decentralized process that involves a high-degree of reinvention and it involves the generation and diffusion of many relevant innovations. The project also identified scholarly communication and inter-organization networks as new types of communication channels that are not well accounted for in existing DOI theories.

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3

Ashiq, Murtaza, Qurat Ul Ain Saleem, and Muhammad Asim. "The Perception of Library and Information Science (LIS) Professionals about Research Data Management Services in University Libraries of Pakistan." Libri 71, no. 3 (April 30, 2021): 239–49. http://dx.doi.org/10.1515/libri-2020-0098.

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Abstract Research data management services (RDMS) is considered as an emerging and groundbreaking area for research libraries. A large number of studies focused on researchers’ perspectives of how they perform research data management practices. There are some studies that examine this important area of research from library and information science (LIS) professionals’ context, especially developing countries like Pakistan. Hence, this study addresses the gap and investigate the RDMS training needs, motivational factors, possible hindrances, and key reasons to support RDMS. A survey method was used and a self-developed questionnaire was prepared using Google Docs survey. The questionnaire link was shared with LIS professionals considering purposive sampling technique. The study highlights the main RDMS supporting reasons, needed training areas, best methods to get training, the motivational factors, and possible hindrances while planning and implementing RDMS. This study fills the gap and addresses research data management literature in developing countries’ context, especially Pakistan, and established that RDMS are poorly observed in developing countries and require some drastic steps to be launched and improved. Higher Education Commission/departments, university administrations, and donor agencies take such initiatives that research data should be openly available through repositories and the maximum number of training opportunities should be provided to LIS professionals.

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4

Craig, Marveen. "Honoring Our Heritage: Shirley J. Staiano, MS, RDMS, RDCS." Journal of Diagnostic Medical Sonography 22, no. 3 (May 2006): 157–58. http://dx.doi.org/10.1177/8756479306288868.

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5

Fitschen, Timm, Alexander Schlemmer, Daniel Hornung, Henrik tom Wörden, Ulrich Parlitz, and Stefan Luther. "CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows." Data 4, no. 2 (June 10, 2019): 83. http://dx.doi.org/10.3390/data4020083.

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We present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data in a FAIR way. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless, it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of its data model, the CaosDB Server and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it.

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6

Jiang, Zhang, Amir Kalev, Wojciech Mruczkiewicz, and Hartmut Neven. "Optimal fermion-to-qubit mapping via ternary trees with applications to reduced quantum states learning." Quantum 4 (June 4, 2020): 276. http://dx.doi.org/10.22331/q-2020-06-04-276.

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We introduce a fermion-to-qubit mapping defined on ternary trees, where any single Majorana operator on an n-mode fermionic system is mapped to a multi-qubit Pauli operator acting nontrivially on ⌈log3⁡(2n+1)⌉ qubits. The mapping has a simple structure and is optimal in the sense that it is impossible to construct Pauli operators in any fermion-to-qubit mapping acting nontrivially on less than log3⁡(2n) qubits on average. We apply it to the problem of learning k-fermion reduced density matrix (RDM), a problem relevant in various quantum simulation applications. We show that one can determine individual elements of all k-fermion RDMs in parallel, to precision ϵ, by repeating a single quantum circuit for ≲(2n+1)kϵ−2 times. This result is based on a method we develop here that allows one to determine individual elements of all k-qubit RDMs in parallel, to precision ϵ, by repeating a single quantum circuit for ≲3kϵ−2 times, independent of the system size. This improves over existing schemes for determining qubit RDMs.

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7

Gavrilov, V. B., I. A. Golutvin, O. L. Kodolova, Vladimir V. Korenkov, L. G. Levchuk, S. V. Shmatov, E. A. Tikhonenko, and V. E. Zhiltsov. "RDMS CMS computing: current status and plans." Computer Research and Modeling 7, no. 3 (June 2015): 395–98. http://dx.doi.org/10.20537/2076-7633-2015-7-3-395-398.

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8

Gavrilov, V., I. Golutvin, O. Kodolova, V. Korenkov, L. Levchuk, S. Shmatov, E. Tikhonenko, and V. Zhiltsov. "Status of RDMS CMS computing." Physics of Particles and Nuclei Letters 13, no. 5 (September 2016): 718–20. http://dx.doi.org/10.1134/s1547477116050216.

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9

Lewiss, Resa E., and Michael Stone. "RDMS Certification for Emergency Physicians." Annals of Emergency Medicine 63, no. 5 (May 2014): 629–30. http://dx.doi.org/10.1016/j.annemergmed.2013.09.031.

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10

Milinkovitch, Michel C., Liana Manukyan, Adrien Debry, Nicolas Di-Poï, Samuel Martin, Daljit Singh, Dominique Lambert, and Matthias Zwicker. "Crocodile Head Scales Are Not Developmental Units But Emerge from Physical Cracking." Science 339, no. 6115 (November 29, 2012): 78–81. http://dx.doi.org/10.1126/science.1226265.

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Various lineages of amniotes display keratinized skin appendages (feathers, hairs, and scales) that differentiate in the embryo from genetically controlled developmental units whose spatial organization is patterned by reaction-diffusion mechanisms (RDMs). We show that, contrary to skin appendages in other amniotes (as well as body scales in crocodiles), face and jaws scales of crocodiles are random polygonal domains of highly keratinized skin, rather than genetically controlled elements, and emerge from a physical self-organizing stochastic process distinct from RDMs: cracking of the developing skin in a stress field. We suggest that the rapid growth of the crocodile embryonic facial and jaw skeleton, combined with the development of a very keratinized skin, generates the mechanical stress that causes cracking.

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11

Lee, Byung-Kwan, and Eun-Hee Jeong. "Design of Rough Set Theory Based Disease Monitoring System for Healthcare." Journal of Korean Institute of Communications and Information Sciences 38C, no. 12 (December 31, 2013): 1095–105. http://dx.doi.org/10.7840/kics.2013.38c.12.1095.

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12

Gaspari, Romolo J., and John Bailitz. "RDMS Ultrasound Certification for Emergency Physicians." Annals of Emergency Medicine 63, no. 5 (May 2014): 628–29. http://dx.doi.org/10.1016/j.annemergmed.2013.11.006.

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13

Subba, Jeny, Basanta Kumar Rai, Dil Kumar Limbu, and Sangen Ruma Rai. "Evaluation of fermentation potential of wild and UV-mutated yeasts screened from traditional murcha." Himalayan Journal of Science and Technology 2 (December 1, 2018): 84–89. http://dx.doi.org/10.3126/hijost.v2i0.25848.

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Murcha (an amylolytic starter) from different parts of Eastern Nepal were screened for fermentative yeasts. The most potential one was UV-mutated (8W lamp at λ = 254 nm and an intensity of 44.21 Wm-2 for 5-50 s) to study the effect of mutation on growth and fermentation properties. Respiratory-deficient mutants (RDMs) that resulted from the mutation were identified by triphenyl tetrazolium chloride (TTC) overlay technique and replica-plated for further isolation. Cell growth, substrate utilization, and ethanol yield of the mutants were compared with normal cells by carrying out fermentation in high-test cane molasses broth of 30 °Bx. An exhaustive screening of the samples resulted in only two murcha viz., from Laxmimarga (LM) and Udayapur (UD), having the desirable fermentation properties. UV-mutation study of UD and LM yeasts (both identified as strains of Saccharomyces cerevisiae) showed 8-12% survival and ~ 22% RDMs yield of the survived cells. Out of the 8 randomly selected RDMs, only UDm4 (colony No. 4 from UD) showed fermentation properties worth further investigation. Comparison of UD, LM and UDm4 by fermenting molasses (high test) broth of 30°Bx showed the least growth of UDm4 but the highest alcohol yield (9% and 16% more compared to UD and LM, respectively). The present finding indicates that it is possible to improve fermentation properties of feral yeasts from murcha by relatively simple UV-mutation approach. Finding the right mutant (the selective screening part), however, may involve considerable time and effort.

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Hess, David A., and Michael J. Rieder. "The Role of Reactive Drug Metabolites in Immune-Mediated Adverse Drug Reactions." Annals of Pharmacotherapy 31, no. 11 (November 1997): 1378–87. http://dx.doi.org/10.1177/106002809703101116.

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OBJECTIVE: To highlight recent advances in the understanding of adverse drug reactions (ADRs), with a focus on models outlining interactions between drug metabolism, disease processes, and immunity. Specific mechanisms that identify the metabolic pathways responsible for drug bioactivation to reactive drug metabolites (RDMs) involved in the initiation and propagation of specific immune-mediated hypersensitivity reactions are discussed. Drug classes well known to be associated with immune-mediated ADRs are reviewed and the clinical implications of current research are discussed. DATA SOURCES: Original experimental research and immunologic review articles relevant to ADR diagnosis and etiology. DATA EXTRACTION: Results of relevant in vitro experiments and clinical reactions to drug therapy were compiled and reviewed. Critical discoveries concerning the identification of RDMs involved in ADRs were highlighted, with respect to RDM involvement in the production of an immune response to drug haptens. DATA SYNTHESIS: Drug adverse effects are classified according to clinical characteristics, immune interactions, and mechanistic similarities. Cytochrome P450 bioactivation of drug molecules to RDMs is a prerequisite to many ADRs. An electrophilic metabolite may react with cellular macromolecules (i.e., lipids, proteins, nucleic acids), resulting in direct cellular damage and organ toxicity. Covalent binding of an RDM to cellular macromolecules may also result in the formation of a hapten that is capable of eliciting a cellular or humoral immune response against drug or protein epitopes, culminating in the characteristic symptoms of hypersensitivity reactions. Mechanistic details concerning the identification of stable protein-metabolite conjugates and their interaction with the immune system remain unclear. Genetic imbalance between bioactivation and detoxification pathways, as well as reduced cellular defense against RDMs due to disease or concomitant drug therapy, act as risk factors to the onset and severity of ADRs. CONCLUSIONS: Adverse reactions to drug therapy cause significant morbidity and mortality. Identification of the pathways involved in drug bioactivation and detoxification may elucidate the potential of chemical agents to induce immune-mediated ADRs. Understanding the mechanisms of ADRs to current xenobiotics is helpful in the prevention and management of ADRs, and may prove useful in the design of novel therapeutic agents with reduced incidence of severe adverse events.

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15

Fleischer, Arthur C. "Donna M. Kepple, RT, RDMS, 1949-2010." Journal of Ultrasound in Medicine 29, no. 6 (June 2010): 1014. http://dx.doi.org/10.7863/jum.2010.29.6.1014.

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16

Makanda, Koleka, Stanley Nzama, and Thokozani Kanyerere. "Policy Implementation for Water Resources Protection: Assessing Spatio-Temporal Trends of Results from Process-Based Outcomes of Resource-Directed Measures Projects in South Africa." Water 14, no. 20 (October 20, 2022): 3322. http://dx.doi.org/10.3390/w14203322.

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Governments are continuously developing strategies for policy implementation toward water resource protection. However, little is known about the practical application of such plans to test their effectiveness in policy practice. This study focused on resource-directed measures (RDMs) in South Africa to assess progress made on policy implementation for water resource protection. The study included document surveys and content analysis of the publicly available reports and documents sourced from state departments and government websites. The findings of the study indicated that water resource-directed measures are used as policy implementation strategies for water resource protection in the country. Furthermore, the study revealed that significant progress has been made in this regard, when a multi-sectorial policy implementation practice approach through public-private partnerships ensured that 69% of the catchments have process-based RDM projects completed, while 18% are in progress, and only 13% are outstanding. In addition, it was found that water resource classes (WRC), numerical limits and ecological conditions for water resource reserve, and numerical limits and narrative statements for resource quality objectives (RQOs) are intermediate outputs originating from RDMs projects. The study recommends that outputs derived from process-based policy implementation plans must be applied at the water resource level and monitored to assess the effectiveness of policies for their effects on the status of water resources.

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17

Azeroual, Otmane. "Treatment of Bad Big Data in Research Data Management (RDM) Systems." Big Data and Cognitive Computing 4, no. 4 (October 18, 2020): 29. http://dx.doi.org/10.3390/bdcc4040029.

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Databases such as research data management systems (RDMS) provide the research data in which information is to be searched for. They provide techniques with which even large amounts of data can be evaluated efficiently. This includes the management of research data and the optimization of access to this data, especially if it cannot be fully loaded into the main memory. They also provide methods for grouping and sorting and optimize requests that are made to them so that they can be processed efficiently even when accessing large amounts of data. Research data offer one thing above all: the opportunity to generate valuable knowledge. The quality of research data is of primary importance for this. Only flawless research data can deliver reliable, beneficial results and enable sound decision-making. Correct, complete and up-to-date research data are therefore essential for successful operational processes. Wrong decisions and inefficiencies in day-to-day operations are only the tip of the iceberg, since the problems with poor data quality span various areas and weaken entire university processes. Therefore, this paper addresses the problems of data quality in the context of RDMS and tries to shed light on the solution for ensuring data quality and to show a way to fix the dirty research data that arise during its integration before it has a negative impact on business success.

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18

Donner, Eva Katharina. "Research data management systems and the organization of universities and research institutes: A systematic literature review." Journal of Librarianship and Information Science, February 1, 2022, 096100062110702. http://dx.doi.org/10.1177/09610006211070282.

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New technological developments, the availability of big data, and the creation of research platforms open a variety of opportunities to generate, store, and analyze research data. To ensure the sustainable handling of research data, the European Commission as well as scientific commissions have recently highlighted the importance of implementing a research data management system (RDMS) in higher education institutes (HEI) which combines technical as well as organizational solutions. A deep understanding of the requirements of research data management (RDM), as well as an overview of the different stakeholders, is a key prerequisite for the implementation of an RDMS. Based on a scientific literature review, the aim of this study is to answer the following research questions: “What organizational factors need to be considered when implementing an RDMS? How do these organizational factors interact with each other and how do they constrain or facilitate the implementation of an RDMS?” The structure of the analysis is built on the four components of Leavitt’s classical model of organizational change: task, structure, technology, and people. The findings reveal that the implementation of RDMS is strongly impacted by the organizational structure, infrastructure, labor culture as well as strategic considerations. Overall, this literature review summarizes different approaches for the implementation of an RDMS. It also identifies areas for future research.

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19

"Keeping the machines running." Machinery 179, no. 4303 (February 2021): 23–25. http://dx.doi.org/10.12968/s0368-8941(22)90009-9.

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20

Matoušek, Mikuláš, Michal Hapka, Libor Veis, and Katarzyna Pernal. "Toward more accurate adiabatic connection approach for multireference wave functions." Journal of Chemical Physics, January 10, 2023. http://dx.doi.org/10.1063/5.0131448.

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A multiconfigurational adiabatic connection (AC) formalism is an attractive approach to computing dynamic correlation within CASSCF and DMRG models. Practical realizations of AC have been based on two approximations: i) fixing one- and two-electron reduced density matrices (1- and 2-RDMs) at the zero-coupling constant limit and ii) extended random phase approximation (ERPA). This work investigates the effect of removing the "fixed-RDM" approximation in AC. The analysis is carried out for two electronic Hamiltonian partitionings: the group product function- and the Dyall-Hamiltonians. Exact reference AC integrands are generated from the DMRG FCI solver. Two AC models are investigated, employing either exact 1- and 2-RDMs or their second-order expansions in the coupling constant in the ERPA equations. Calculations for model molecules indicate that lifting the fixed-RDM approximation is a viable way toward improving accuracy of the existing AC approximations.

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21

Yadan, Zhang, Lian Xin, and Wu Jian. "Solving the inverse problem in electrocardiography imaging for atrial fibrillation using various time-frequency decomposition techniques based on empirical mode decomposition: A comparative study." Frontiers in Physiology 13 (November 2, 2022). http://dx.doi.org/10.3389/fphys.2022.999900.

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Electrocardiographic imaging (ECGI) can aid in identifying the driving sources that cause and sustain atrial fibrillation (AF). Traditional regularization strategies for addressing the ECGI inverse problem are not currently concerned about the multi-scale analysis of the inverse problem, and these techniques are not clinically reliable. We have previously investigated the solution based on uniform phase mode decomposition (UPEMD-based) to the ECGI inverse problem. Numerous other methods for the time-frequency analysis derived from empirical mode decomposition (EMD-based) have not been applied to the inverse problem in ECGI. By applying many EMD-based solutions to the ECGI inverse problem and evaluating the performance of these solutions, we hope to find a more efficient EMD-based solution to the ECGI inverse problem. In this study, five AF simulation datasets and two real datasets from AF patients derived from a clinical ablation procedure are employed to evaluate the operating efficiency of several EMD-based solutions. The Pearson’s correlation coefficient (CC), the relative difference measurement star (RDMS) of the computed epicardial dominant frequency (DF) map and driver probability (DP) map, and the distance (Dis) between the estimated and referenced most probable driving sources are used to evaluate the application of various EMD-based solutions in ECGI. The results show that for DF maps on all simulation datasets, the CC of UPEMD-based and improved UPEMD (IUPEMD)-based techniques are both greater than 0.95 and the CC of the empirical wavelet transform (EWT)-based solution is greater than 0.889, and the RDMS of UPEMD-based and IUPEMD-based approaches is less than 0.3 overall and the RDMS of EWT-based method is less than 0.48, performing better than other EMD-based solutions; for DP maps, the CC of UPEMD-based and IUPEMD-based techniques are close to 0.5, the CC of EWT-based is 0.449, and the CC of the remaining EMD-based techniques on the SAF and CAF is all below 0.1; the RDMS of UPEMD-based and IUPEMD-based are 0.06∼0.9 less than that of other EMD-based methods for all the simulation datasets overall. On two authentic AF datasets, the Dis between the first 10 real and estimated maximum DF positions of UPEMD-based and EWT-based methods are 212∼1440 less than that of others, demonstrating these two EMD-based solutions are superior and are suggested for clinical application in solving the ECGI inverse problem. On all datasets, EWT-based algorithms deconstruct the signal in the shortest time (no more than 0.12s), followed by UPEMD-based solutions (less than 0.81s), showing that these two schemes are more efficient than others.

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22

Ye, Yishan, Zhenmiao Deng, Pingping Pan, Weijie Ma, and Xiaohong Huang. "Doppler-Spread Targets Detection for FMCW Radar Using Concurrent RDMs." IEEE Transactions on Vehicular Technology, 2022, 1–12. http://dx.doi.org/10.1109/tvt.2022.3190478.

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23

Begum, Shaida. "An Invigorating Perspective of a Practical Aspect of NoSQL Database." International Journal of Research Publication and Reviews, September 19, 2022, 1000–1004. http://dx.doi.org/10.55248/gengpi.2022.3.9.29.

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The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

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Begum, Shahida. "A Deeper Examination of NoSQL Database Models and Characteristics." International Journal of Research Publication and Reviews, October 20, 2022, 1510–14. http://dx.doi.org/10.55248/gengpi.2022.3.10.56.

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The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

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Begum, Shaida. "An Invigorating Perspective of a Practical Aspect of NoSQL Database." International Journal of Research Publication and Reviews, October 22, 2022, 1645–49. http://dx.doi.org/10.55248/gengpi.2022.3.10.57.

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The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

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26

HHM, Darweesh. "Characterization of Coir Pith Ash Blended Cement Pastes." Research & Development in Material Science 15, no. 1 (March 4, 2021). http://dx.doi.org/10.31031/rdms.2021.15.000851.

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LZ, Chhangte. "Annual Inhalation Dose of Indoor Radon and Thoron in Mizoram, India." Research & Development in Material Science 15, no. 1 (March 5, 2021). http://dx.doi.org/10.31031/rdms.2021.15.000852.

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Murata, Yoshifumi. "Disintegration Properties and Drug Release Profiles of Sodium Alginate Films Containing Rebamipide." Research & Development in Material Science 15, no. 1 (March 24, 2021). http://dx.doi.org/10.31031/rdms.2021.15.000853.

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Bratovcic, Amra. "Heterogeneous Photocatalysts Based on TiO2 for Abatement of Hazardous Air Pollutants." Research & Development in Material Science 14, no. 5 (February 15, 2021). http://dx.doi.org/10.31031/rdms.2021.14.000850.

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Orion, Itzhak. "Micron-scale Monte Carlo Simulations for Additive Manufacturing using Electron Beam." Research & Development in Material Science 14, no. 5 (January 22, 2021). http://dx.doi.org/10.31031/rdms.2021.14.000849.

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MS, Islam. "Nonwoven Hemp Fibre Reinforced Acrodur Biocomposites and their Mechanical Performance in Immersed Water." Research & Development in Material Science 14, no. 3 (November 20, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000838.

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Ahmed, Ash. "Impact of Nitrate Vulnerable Zones and Catchment Sensitive Farming on Water Quality in UK: Case Study of Ingbirchworth and Scout Dyke Reservoirs." Research & Development in Material Science 14, no. 5 (January 15, 2021). http://dx.doi.org/10.31031/rdms.2021.14.000848.

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Turan, M. Deniz. "Technological Research of Zinc Extraction from Elazığ Simitsonite Ore." Research & Development in Material Science 14, no. 3 (November 20, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000839.

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M, Abuziarov. "About Application of the Overset Grid Method for Fluid Structure Interaction Problems in 3D Codes Based on Modification of Godunov Scheme Uniform for Both CFD and CSD." Research & Development in Material Science 14, no. 4 (December 14, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000843.

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Ounaies, Mohamed. "Pressure Effect on Humidity Diffusion through Polyester/Glass Fiber Composite in Sea Water." Research & Development in Material Science 14, no. 4 (December 17, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000845.

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II, Aliyev. "Study of Phase Formation In The Cute-As2te3System." Research & Development in Material Science 14, no. 4 (December 14, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000844.

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Oueslati, Abderrazek. "Electrical Conductivity and Dielectric Properties of Lead‑Free Perovskite: Ca0,08Na0,92Sn0,08Nb0,92O3 Ceramic." Research & Development in Material Science 14, no. 5 (December 21, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000847.

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Udell, Kent S. "Permeability of MgCl2- Graphite Reactive Compound." Research & Development in Material Science 14, no. 4 (December 14, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000842.

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Aydi, Abdelhedi. "Vibrational Analysis and Electrical Conduction Mechanism Investigations ofOrder Disorder Phase Transitions in [N(C3H7)4]2ZnBr4Compound." Research & Development in Material Science 14, no. 3 (November 13, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000837.

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Roy, Prasun Kumar. "Structural Firefighting Suits: Futuristic Materials and Designs for Enhanced Comfort." Research & Development in Material Science 14, no. 4 (November 24, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000841.

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Abdel Fattah, Wafa I. "Grape Seeds the Nontraditional Source for Achieving Bio-Nanomaterials Addressing Antibacterial, Anticancer and Antidiabetic Functions." Research & Development in Material Science 14, no. 5 (December 21, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000846.

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Farouk, Mohamed Ashraf. "Comments on the Basis of Wave Mechanics." Research & Development in Material Science 14, no. 2 (November 10, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000835.

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Khan, Nida Tabassum. "World of Nanobionics." Research & Development in Material Science 14, no. 3 (November 24, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000840.

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T, Hickmann. "Bipolar Plates Injection Molded for Series Production." Research & Development in Material Science 14, no. 3 (November 11, 2020). http://dx.doi.org/10.31031/rdms.2020.14.000836.

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Baykara, Tarık. "A Generic Transformation of Advanced Materials Science and Technologies: Towards Multi-Functional and Intelliqent Multi-Materials Systems." Research & Development in Material Science 1, no. 1 (August 18, 2017). http://dx.doi.org/10.31031/rdms.2017.01.000501.

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Zhang, Jianbo. "Carbon Nanomaterials: Opening a Path to a Sustainable Future." Research & Development in Material Science 1, no. 1 (September 8, 2017). http://dx.doi.org/10.31031/rdms.2017.01.000502.

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Mishra, Shrawan K. "Emergent Interfacial Magnetic Skyrmions and Its Technological Importance." Research & Development in Material Science 1, no. 1 (September 18, 2017). http://dx.doi.org/10.31031/rdms.2017.01.000503.

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Tamir, Abraham. "Thermodynamics and Its Basic Processes via Art." Research & Development in Material Science 1, no. 1 (September 18, 2017). http://dx.doi.org/10.31031/rdms.2017.01.000504.

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Singh, Rajesh Kumar. "Corrosion Protection Polybutadiene-Coated Mild Steel in Marine Water by Nanocoating and Filler Compounds." Research & Development in Material Science 1, no. 1 (September 27, 2017). http://dx.doi.org/10.31031/rdms.2017.01.000505.

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Gunamalai, Lavanya. "Collagen-The Biomaterial." Research & Development in Material Science 1, no. 2 (September 22, 2017). http://dx.doi.org/10.31031/rdms.2017.01.000506.

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