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Journal articles on the topic 'Data management'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Gade, Kishore. "Data Analytics: Data Mesh Architecture and Its Implications for Data Management." International Journal of Science and Research (IJSR) 8, no. 11 (2019): 2061–67. http://dx.doi.org/10.21275/sr19113110630.

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2

Sri Vardhan Pentapati, Harsha. "Management Data and Business Analytics." International Journal of Science and Research (IJSR) 10, no. 9 (2021): 1603–7. https://doi.org/10.21275/sr21927211738.

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3

Moreno, P., M. Ruiz, and F. J. Gorines. "TBM Process Data Management System." International Journal of Engineering and Technology 7, no. 5 (2015): 431–34. http://dx.doi.org/10.7763/ijet.2015.v7.832.

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4

Reddy Desani, Nithin. "Enhancing Data Governance through AI - Driven Data Quality Management and Automated Data Contracts." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 2519–25. http://dx.doi.org/10.21275/es23812104904.

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5

ЛИТОВЧЕНКО, В. Б., Д. В. ЗЕНКОВА, and К. В. ВОВК. "DATA-DRIVEN IN MANAGEMENT." Экономика и предпринимательство, no. 7(168) (August 6, 2024): 1060–64. http://dx.doi.org/10.34925/eip.2024.168.7.211.

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В данной статье рассмотрены работа и применение Data-Driven в менеджменте. Data-driven (англ. «управляемый данными») - это новый подход к управлению, основывающийся на собираемых данных. Только они имеют значение, при принятии того или иного решения для бизнеса. Dataориентированную компанию отличает умение её сотрудников анализировать данные, учитывая рыночный контекст. В статье подробно описано как начать использовать данный подход, какими инструментами следует пользоваться, на что делать акцент. Также приведены конкретные примеры применения в пяти успешных компаниях с подробным описанием и в

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6

Sashi, K., and Antony Selvadoss Thanamani. "Dynamic Replica Management for Data Grid." International Journal of Engineering and Technology 2, no. 4 (2010): 329–33. http://dx.doi.org/10.7763/ijet.2010.v2.142.

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7

I. Pachankis, Yang. "Three-Body Logging for Data Management." Journal of Theoretical Physics & Mathematics Research 1, no. 1 (2023): 01–06. https://doi.org/10.64030/3065-8802.01.01.01.

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The research develops a logging method in observational astronomy from a nuclear science perspective. It takes the existing optics based observational parameters to a three-body parameter in the observational vantage points. By taking the earth’s atmosphere as a positive meniscus lens, the celestial plane and the sun-earth-moon three-body plane are differentiated in dimensions. The complex planes are differentiated between observational preconditions and data depth in light quanta, and the light quanta in data structure can be further corrected to motion biases and gravitational lensing. The a

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8

Iqbal, Ms Snober, K. SreeNiketh Raj, K. Vineeth Reddy, and Mohd Mudaseer Mazharuddin. "Decentralized Education Data Management Using Blockchain." International Journal of Research Publication and Reviews 6, no. 6 (2025): 3173–81. https://doi.org/10.55248/gengpi.6.0625.2122.

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9

Lee, Jonghyun Harry, Tyler Hesser, Matthew Farthing, Spicer Bak, and Katherine DeVore. "SCALABLE REAL-TIME DATA ASSIMILATION WITH VARIOUS DATA TYPES FOR ACCURATE SPATIOTEMPORAL NEARSHORE BATHYMETRY ESTIMATION." Coastal Engineering Proceedings, no. 37 (October 2, 2023): 156. http://dx.doi.org/10.9753/icce.v37.management.156.

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Immediate estimation of nearshore bathymetry is crucial for accurate prediction of nearshore wave conditions and coastal flooding events. However, direct bathymetry data collection is expensive and time-consuming, while accurate airborne lidar-based survey is limited by breaking waves and decreased light penetration affected by water turbidity. Several recent efforts have been made to apply interpolation and inverse modeling approaches to indirect remote sensed observations along with sparse direct survey data points. Example indirect observations include video-based observations such as time-

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10

Briney, Kristin, Heather Coates, and Abigail Goben. "Foundational Practices of Research Data Management." Research Ideas and Outcomes 6 (July 27, 2020): e56508. https://doi.org/10.3897/rio.6.e56508.

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The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity. Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-co

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11

Meseck, Reed M. "Data management." ACM SIGMOD Record 30, no. 2 (2001): 569–70. http://dx.doi.org/10.1145/376284.375745.

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12

Thompson, Cheryl Bagley, and Edward A. Panacek. "Data Management." Air Medical Journal 27, no. 4 (2008): 156–58. http://dx.doi.org/10.1016/j.amj.2008.05.001.

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13

Marchant, David. "Data Management." Museum Management and Curatorship 18, no. 2 (1999): 197–201. http://dx.doi.org/10.1080/09647779900801802.

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14

Kõljalg, Urmas, Kessy Abarenkov, Allan Zirk, et al. "PlutoF: Biodiversity data management platform for the complete data lifecycle." Biodiversity Information Science and Standards 3 (June 26, 2019): e37398. https://doi.org/10.3897/biss.3.37398.

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PlutoF online platform (https://plutof.ut.ee) is built for the management of biodiversity data. The concept is to provide a common workbench where the full data lifecycle can be managed and support seamless data sharing between single users, workgroups and institutions. Today, large and sophisticated biodiversity datasets are increasingly developed and managed by international workgroups. PlutoF's ambition is to serve such collaborative projects as well as to provide data management services to single users, museum or private collections and research institutions. Data management in PlutoF fol

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15

McDonald, John. "Records Management and Data Management." Records Management Journal 1, no. 1 (1989): 4–11. http://dx.doi.org/10.1108/eb027016.

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16

Yasmeen, Mrs. "NOSQL Database Engines for Big Data Management." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 617–22. http://dx.doi.org/10.31142/ijtsrd18608.

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17

Robinson, Robert. "Clinical Data Management: The Fifth Deflection Point." International Journal of Science and Research (IJSR) 11, no. 10 (2022): 515–16. http://dx.doi.org/10.21275/sr221011132951.

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18

Gadiko, Aditya. "Modernizing Clinical Data Management with Artificial Intelligence." International Journal of Science and Research (IJSR) 11, no. 12 (2022): 1323–26. http://dx.doi.org/10.21275/sr24520232328.

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19

Ramadoss, Gokul. "Better Provider Data Management to Improve Referrals." International Journal of Science and Research (IJSR) 11, no. 1 (2022): 1669–72. http://dx.doi.org/10.21275/sr24820001258.

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20

Patnaik, Raja. "Large - Scale Data Management with Salesforce APIs." International Journal of Science and Research (IJSR) 11, no. 2 (2022): 1339–43. http://dx.doi.org/10.21275/sr24529174912.

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21

Van, Tuyl Steven, and Amanda Whitmire. "Investigation of Non-Academic Data Management Practices to Inform Academic Research Data Management." Research Ideas and Outcomes 4 (October 31, 2018): e30829. https://doi.org/10.3897/rio.4.e30829.

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In recent years, the academic research data management (RDM) community has worked closely with funding agencies, university administrators, and researchers to develop best practices for RDM. The RDM community, however, has spent relatively little time exploring best practices used in non-academic environments (industry, government, etc.) for management, preservation, and sharing of data. In this poster, we present the results of a project wherein we approached a number of non-academic corporations and institutions to discuss how data is managed in those organizations and discern what the acade

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22

Lagoze, Carl, William C. Block, Jeremy Williams, John Abowd, and Lars Vilhuber. "Data Management of Confidential Data." International Journal of Digital Curation 8, no. 1 (2013): 265–78. http://dx.doi.org/10.2218/ijdc.v8i1.259.

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Social science researchers increasingly make use of data that is confidential because it contains linkages to the identities of people, corporations, etc. The value of this data lies in the ability to join the identifiable entities with external data, such as genome data, geospatial information, and the like. However, the confidentiality of this data is a barrier to its utility and curation, making it difficult to fulfil US federal data management mandates and interfering with basic scholarly practices, such as validation and reuse of existing results. We describe the complexity of the relatio

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23

Aiken, Peter, Mark Gillenson, Xihui Zhang, and David Rafner. "Data Management and Data Administration." Journal of Database Management 22, no. 3 (2011): 24–45. http://dx.doi.org/10.4018/jdm.2011070102.

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Data management (DM) has existed in conjunction with software development and the management of the full set of information technology (IT)-related components. However, it has been more than two decades since research into DM as it is practiced has been published. In this paper, the authors compare aspects of DM across a quarter-century timeline, obtaining data using comparable sets of subject matter experts. Using this information to observe the profession’s evolution, the authors have updated the understanding of DM as it is practiced, giving additional insight into DM, including its current

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24

Seegers, G., H. Kempff, and C. J. Van Rees. "Data management and data analysis." Poetics 16, no. 6 (1987): 535–52. http://dx.doi.org/10.1016/0304-422x(87)90016-7.

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25

Jitendra, Choudhary, and Kulkarni Akanksha. "Data Management vs Data Governance." International Journal of Innovative Science and Research Technology 7, no. 11 (2022): 1346–47. https://doi.org/10.5281/zenodo.7453533.

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Data management is the practice of gathering, organizing, protecting, sharing and storing data so it can be analyzed for business decisions. It helps improve data scalability, visibility, security and reliability. With the vast quantity of data available to organizations, it has become essential for organizations to use data management to generate maximum value from the data available to them. Data governance is a discipline of data management. It ensures that data is high quality by making it secure, available, usable and consistent. One of the primary goals of data governance is to create ha

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26

Raj Singh, Sakshi, and Mohammad Zunnun Khan. "An Effective Data Management Framework for Healthcare: Big Data Perspective: A Review." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 1382–86. http://dx.doi.org/10.21275/sr22711204346.

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27

LUPU, Viorica. "DATA MANAGEMENT PLAN: IMPORTANCE, CONTENT, PREPARATION." Akademos Journal of Science, Innovation, Culture and Art 2(57) (August 15, 2020): 126–31. https://doi.org/10.5281/zenodo.3989307.

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The importance and role of data management plans has increased in the context of Horizon programs, policies of research funders and journal publishers, which submit requirements for efficient management of research data and ensuring access to them. This article reflects various factors that have led to increased interest in research data management, includes the components that commonly form a data management plan, and provides examples of tools and other resources that can help researchers and others involved in data management.

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28

Bragina, D., and N. Molodchik. "BIG DATA IN HUMAN RESOURCE MANAGEMENT." Management of the Personnel and Intellectual Resources in Russia 9, no. 3 (2020): 76–80. http://dx.doi.org/10.12737/2305-7807-2020-76-80.

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The article discusses the possibilities of using big data in the field of human resources management, shows the difficulties that can be encountered when introducing these technologies into the work of the company. The main problems of the use of employee data by companies for the analysis, forecasting and improvement production indicators are given. Examples of companies that successfully use big data in their work are shown. Recommendations how to introduce the technology of big data analysis in the field of human resource management are given.

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29

Alok, Reddy Jakkula. "Effective Data Management Strategies in E-commerce." Journal of Scientific and Engineering Research 6, no. 1 (2019): 252–54. https://doi.org/10.5281/zenodo.11234631.

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In the rapidly evolving e-commerce landscape, effective data management has become a critical success factor. This research paper examines the challenges and strategic solutions for managing data in the e-commerce sector. It highlights the importance of data quality, security, integration, and analytics in shaping customer-centric e-commerce platforms. Through a comprehensive literature review and case studies, this study offers actionable insights for e-commerce businesses to leverage data as a strategic asset and drive sustainable growth.

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30

Goben, Abigail, and Kristin Briney. "Data Management Books for Researchers - An Annotated Bibliography." Research Ideas and Outcomes 11 (May 14, 2025): e154845. https://doi.org/10.3897/rio.11.e154845.

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While funders and publishers continue to expand requirements for data management planning and sharing, few books have been written for academic researchers and research trainees to help them understand both introductory or discipline-specific concepts and practices. In this annotated bibliography, we review currently available English-language data management books and identify the limitations and opportunities for future publications.

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31

Kiehn, Felix, Mareike Schmidt, Daniel Glake, et al. "Polyglot data management." Proceedings of the VLDB Endowment 15, no. 12 (2022): 3750–53. http://dx.doi.org/10.14778/3554821.3554891.

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Due to the increasing variety of the current database landscape, polyglot data management has become a hot research topic in recent years. The underlying idea is to combine the benefits of different data stores behind a predefined set of common interfaces and thus address use cases that individual stores cannot meet. This can be accomplished using different approaches which vary greatly in terms of capabilities, functionality, and architectural concepts. This tutorial provides a detailed overview of the current state of research in polyglot data management. We motivate its use by showing the h

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32

Surkis, Alisa, and Kevin Read. "Research data management." Journal of the Medical Library Association : JMLA 103, no. 3 (2015): 154–56. http://dx.doi.org/10.3163/1536-5050.103.3.011.

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33

Kasemsap, Kijpokin. "Big Data Management." International Journal of Organizational and Collective Intelligence 7, no. 3 (2017): 44–55. http://dx.doi.org/10.4018/ijoci.2017070104.

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The objective of this article is to provide the advanced issues and approaches of big data management. The literature review indicates the overview of big data management; the aspects of Big Data Analytics (BDA); the importance of big data management; the methods for big data management; the privacy and security concerns of big data management; and the big data management in the health care industry. Organizations that have been successful in working with effective big data management have accomplished this issue using data to help make sense of the information. The volume of data that compani

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34

Denney, Dennis. "Subsea-Data Management." Journal of Petroleum Technology 59, no. 12 (2007): 75–77. http://dx.doi.org/10.2118/1207-0075-jpt.

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35

YU, Min. "P2P Data Management." Journal of Software 17, no. 8 (2006): 1717. http://dx.doi.org/10.1360/jos171717.

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36

Jensen, C. S., and R. T. Snodgrass. "Temporal data management." IEEE Transactions on Knowledge and Data Engineering 11, no. 1 (1999): 36–44. http://dx.doi.org/10.1109/69.755613.

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37

Heuer, Andreas. "Research Data Management." it - Information Technology 62, no. 1 (2020): 1–5. http://dx.doi.org/10.1515/itit-2020-0002.

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38

Madria, S. K., and S. S. Bhowdrick. "Mobile data management." IEEE Potentials 20, no. 4 (2001): 11–15. http://dx.doi.org/10.1109/45.969587.

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39

Hoehl, Karl, Günther Merkl, Uwe Gerberding, Norbert Singer, Markus Schnee, and Gabriele Kille. "Engineering Data Management." ATZextra worldwide 14, no. 1 (2009): 156–61. http://dx.doi.org/10.1365/s40111-009-0148-4.

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40

Nargesian, Fatemeh, Erkang Zhu, Renée J. Miller, Ken Q. Pu, and Patricia C. Arocena. "Data lake management." Proceedings of the VLDB Endowment 12, no. 12 (2019): 1986–89. http://dx.doi.org/10.14778/3352063.3352116.

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41

Meliou, Alexandra, Wolfgang Gatterbauer, and Dan Suciu. "Reverse data management." Proceedings of the VLDB Endowment 4, no. 12 (2011): 1490–93. http://dx.doi.org/10.14778/3402755.3402803.

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42

Stoyanovich, Julia, Bill Howe, and H. V. Jagadish. "Responsible data management." Proceedings of the VLDB Endowment 13, no. 12 (2020): 3474–88. http://dx.doi.org/10.14778/3415478.3415570.

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43

Troisi, Orlando, Mario D’Arco, Francesca Loia, and Gennaro Maione. "Big data management." International Journal of Engineering Business Management 10 (January 2018): 184797901876777. http://dx.doi.org/10.1177/1847979018767776.

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44

Mokbel, Mohamed, Chi-Yin Chow, and Walid Aref. "Location data management." Proceedings of the VLDB Endowment 9, no. 13 (2016): 1622. http://dx.doi.org/10.14778/3007263.3007327.

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45

Palpanas, Themis. "Data Series Management." ACM SIGMOD Record 44, no. 2 (2015): 47–52. http://dx.doi.org/10.1145/2814710.2814719.

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46

Kannan, K. L., and L. R. Baylor. "ATF data management." Review of Scientific Instruments 59, no. 8 (1988): 1783–85. http://dx.doi.org/10.1063/1.1140111.

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47

Khurana, Purnima, and Purnima Bindal. "Test Data Management." International Journal of Computer Trends and Technology 15, no. 4 (2014): 162–67. http://dx.doi.org/10.14445/22312803/ijctt-v15p135.

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48

Clarke, Drew. "Antarctic data management." Antarctic Science 5, no. 3 (1993): 237. http://dx.doi.org/10.1017/s0954102093000318.

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Antarctic science is inter-disciplinary in character, multi-national in execution, and globally relevant. Data management in this environment must be examined from political, scientific and economic perspectives. The Antarctic Treaty calls on parties to exchange and make freely available scientific observations and results from Antarctica, so establishing the political context for addressing data management. The scientific context arises from the increasingly large and complex issues being addressed, including environmental monitoring and global change programmes, while the economic context co

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49

Kaku, Kazuya, Takashi Yamazaki, Toshiaki Hashimoto, and Ryo Tanabe. "Data management/system." Geocarto International 12, no. 4 (1997): 79–85. http://dx.doi.org/10.1080/10106049709354620.

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50

Ausman, R. K., G. D. Baer, M. R. McGuire, et al. "CLINICAL DATA MANAGEMENT*." Annals of the New York Academy of Sciences 128, no. 3 (2006): 1100–1107. http://dx.doi.org/10.1111/j.1749-6632.1965.tb11719.x.

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