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Journal articles on the topic 'The data model'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Jabłoński, Marek. "Open data business model: innovative aspects of designing of business." Studia i Prace WNEiZ 52 (2018): 41–51. http://dx.doi.org/10.18276/sip.2018.52/2-03.

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Vala, Mr Manish, Kajal Patel, and Harsh Lad. "Multi Model Biometrics Data Retrieval Through: Big-Data." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 1273–77. http://dx.doi.org/10.31142/ijtsrd15933.

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3

Shilpa Rani, Shilpa Rani. "Data Model- Building Web Applications." International Journal of Scientific Research 2, no. 3 (2012): 181–82. http://dx.doi.org/10.15373/22778179/mar2013/58.

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4

Deepak, Prajapat, and Kulkarni Akanksha. "Database Management System: Data Model." Journal of Advances in Computational Intelligence Theory 5, no. 2 (2023): 1–9. https://doi.org/10.5281/zenodo.7907613.

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<em>In this paper, we are talking about the modern application require database to catch and execute more in closure semantics than traditional application. In order to collect and analyse data, a database management system communicates with applications, end users, and the database itself. A database is a structured collection of data that is electronically accessible and stored. Large databases are housed on clusters of computers or cloud storage, whilst smaller ones can be maintained on a file system.           Data modelling, effective data

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Gardner, D., K. H. Knuth, M. Abato, et al. "Common Data Model for Neuroscience Data and Data Model Exchange." Journal of the American Medical Informatics Association 8, no. 1 (2001): 17–33. http://dx.doi.org/10.1136/jamia.2001.0080017.

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6

Kamaruddin, Mahamsiatus. "Trialogical Model in Data Analysis Using Spreadsheet." Journal of ICT In Education 7, no. 2 (2020): 54–64. http://dx.doi.org/10.37134/jictie.vol7.2.5.2020.

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The advance of information technology and the availability of data in education management provide an opportunity, to run data analysis to assist top management in decision making. Information technology can transform data analysis in a more concise format, accurate, and in a short time, by using any available office application software which is already installed on the computer. However, even though the management at an operational-level in an organization usually already has the software installed on a computer to support the managing of data, the extent of knowledge and skills to use the s

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Ritika, Singh, Panchani Nilansh, and Bhatnagar Aastha. "Analysis and Simulation of COVID-19." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 7 (2021): 51–54. https://doi.org/10.35940/ijitee.G8928.0510721.

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India is facing a severe second wave of COVID-19 which is much worse than the first wave. It is spreading much faster. India has now surpassed U.S. in terms of daily COVID-19 cases. This paper aims to analyze the trend of COVID 19 and examine why second wave happened and why it is so bad by simulating a simple SEIR model. Which is a compartmental model based on 4 compartments Susceptible, Exposed, Infectious, Recovered.

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Scarciotti, Giordano; Jiang Zhong-Ping; Astolfi Alessandro. "Data-driven constrained optimal model reduction." European Journal of Control 53 (October 24, 2019): 68–78. https://doi.org/10.1016/j.ejcon.2019.10.006.

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Model reduction by moment matching can be interpreted as the problem of finding a reduced-order model which possesses the same steady-state output response of a given full-order system for a prescribed class of input signals. Little information regarding the transient behavior of the system is systematically preserved, limiting the use of reduced-order models in control applications. In this paper we formulate and solve the problem of <em>constrained</em> optimal model reduction. Using a data-driven approach we determine an estimate of the moments and of the transient response of a possibly un

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POPESCU, Ionel, Lică FLORE, and Albert ARNAU CUBILLO. "DERIVATION OF MASS, STIFFNESS AND DAMPING MATRICES OF A MODEL WING FROM EXPERIMENTAL MODAL DATA." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 19, no. 1 (2017): 323–30. http://dx.doi.org/10.19062/2247-3173.2017.19.1.39.

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Ajoudanian, Sh, and M. R. Ahmadi. "A Novel Data Security Model for Cloud Computing." International Journal of Engineering and Technology 4, no. 3 (2012): 326–29. http://dx.doi.org/10.7763/ijet.2012.v4.375.

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11

Oubelli, Lynda Ait, Yamine Aït Ameur, Judicaël Bedouet, Romain Kervarc, Benoît Chausserie-Laprée, and Béatrice Larzul. "A scalable model based approach for data model evolution: Application to space missions data models." Computer Languages, Systems & Structures 54 (December 2018): 358–85. http://dx.doi.org/10.1016/j.cl.2018.08.001.

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12

Li, HuaJun, JunRong Wang, and Sau-Lon James Hu. "Model updating based on incomplete modal data." Science China Technological Sciences 54, no. 7 (2011): 1737–47. http://dx.doi.org/10.1007/s11431-011-4415-z.

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13

Li, Qing. "Object data model = object-oriented + semantic models." Computer Standards & Interfaces 13, no. 1-3 (1991): 99–103. http://dx.doi.org/10.1016/0920-5489(91)90015-r.

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14

Ahmadian, H., G. M. L. Gladwell, and F. Ismail. "Finite Element Model Identification Using Modal Data." Journal of Sound and Vibration 172, no. 5 (1994): 657–69. http://dx.doi.org/10.1006/jsvi.1994.1204.

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15

Mr., Manish Vala, Patel Kajal, and Lad Harsh. "Multi Model Biometrics Data Retrieval Through Big Data." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 1273–77. https://doi.org/10.31142/ijtsrd15933.

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This study is devoted to Big Data problems in biometric technologies. Before the traditional database were not capable to handle the large amount of data, so we highlight the concept of using Big Data technology in solution of a wide range of issues faced by the national security, law enforcement system and defence authorities. The study provides information about Big Data in the field of biometric technology. Mr. Manish Vala | Kajal Patel | Harsh Lad "Multi Model Biometrics Data Retrieval Through: Big-Data" Published in International Journal of Trend in Scientific Research and Devel

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16

Benila S, Benila S., and Usha Bhanu N. Benila S. "Fog Managed Data Model for IoT based Healthcare Systems." 網際網路技術學刊 23, no. 2 (2022): 217–26. http://dx.doi.org/10.53106/160792642022032302003.

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<p>In Internet of things enabled healthcare system, sensors create vast volumes of data that are analyzed in the cloud. Transferring data from the cloud to the application takes a long time. An effective infrastructure can reduce latency and costs by processing data in real-time and close to the user devices. Fog computing can solve this issue by reducing latency by storing, processing, and analyzing patient data at the network edge. Placing the resources at fog layer and scheduling tasks is quite challenging in Fog computing. This paper proposes a Fog Managed Data Model (FMDM) with thre

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17

B, Polaiah, Sunitha M, Rajesh Anand B, and Khadar Babu SK. "Gaussian Auto Regressive Model for Scat-Sat Data Set." International Journal of Science and Research (IJSR) 12, no. 11 (2023): 840–43. http://dx.doi.org/10.21275/sr231111195655.

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18

Gdanskiy, N. I., А. М. Кarpov, and P. Y. Коmova. "Using prediction in filtering data for solving model tasks." Contemporary problems of social work 1, no. 2 (2015): 81–91. http://dx.doi.org/10.17922/2412-5466-2015-1-2-81-91.

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19

BUY, Dmitriy, Sergey POLYAKOV, and Iuliia HRYSHKO. "Formal Specification of the NoSQL Document-Oriented Data Model." Acta Electrotechnica et Informatica 13, no. 4 (2013): 26–31. http://dx.doi.org/10.15546/aeei-2013-0045.

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20

MOSKALENKO, Artem, and Vitaliy OSYADLYI. "MODEL AND METHOD OF ASSESSING THE MEDICAL DATA SUFFICIENCY." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (2022): 19–24. http://dx.doi.org/10.31891/2307-5732-2022-309-3-19-24.

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The conducted state-of-the-art on known decisions on the use of blockchain technologies in the construction of information technology for medical data management showed that, despite a large number of different solutions, effective methods and solutions for medical data management based on blockchain technologies are currently lacking. Thus, improving the efficiency of medical data management by developing methods of medical data management based on blockchain technologies is currently an urgent problem. The aim of this study is to improve the efficiency of medical data management by developin

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21

Vijai, B. "A Semiparametric Mixture Cure Rate Model for Tuberculosis Data." Chettinad Health City Medical Journal 12, no. 04 (2023): 3–11. http://dx.doi.org/10.24321/2278.2044.202364.

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22

Smith, Jane. "CREATING A SEMANTIC METADATA MODEL FOR HUMANITIES RESEARCH DATA." International Research Journal of Library and Information Sciences 2, no. 1 (2025): 1–6. https://doi.org/10.55640/irjlis-v02i01-01.

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This paper presents a novel metadata design for organizing and managing humanities research data based on the semantic structure of research literature. The proposed metadata model aims to enhance the accessibility, discoverability, and interoperability of research data by leveraging the rich, nuanced semantics embedded within humanities texts. By aligning metadata creation with the semantic structure of research literature, this design proposes a system that facilitates the categorization and cross-referencing of knowledge while ensuring that research data is easily retrievable. The paper als

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23

Anusha, L., and G. S. Nagaraja. "Outlier Detection in High Dimensional Data." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 5 (2021): 128–30. https://doi.org/10.35940/ijeat.E2675.0610521.

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Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimens

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24

NASSIRI, Hassana. "Data Model Integration." International Journal of New Computer Architectures and their Applications 7, no. 2 (2017): 45–49. http://dx.doi.org/10.17781/p002327.

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25

Mäkitalo, Niko, Daniel Flores-Martin, Huber Flores, et al. "Human Data Model." ACM Transactions on Computing for Healthcare 1, no. 4 (2020): 1–39. http://dx.doi.org/10.1145/3402524.

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26

Khazraee, Emad, Saeed Moaddeli, Azadeh Sanjari, and Shadi Shakeri. "EIAH data model." Aslib Proceedings 63, no. 1 (2011): 46–56. http://dx.doi.org/10.1108/00012531111103777.

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27

Chandler, Mark, H. Dowsett, and A. Haywood. "The PRISM Model/Data Cooperative: Mid-Pliocene data-model comparisons." PAGES news 16, no. 2 (2008): 24–25. http://dx.doi.org/10.22498/pages.16.2.24.

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28

Laury, Ritva, and Tsuyoshi Ono. "Data is Data and Model is Model: You Don't Discard the Data that Doesn't Fit Your Model!" Language 81, no. 1 (2005): 218–25. http://dx.doi.org/10.1353/lan.2005.0026.

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29

Poornima, Mahadevappa, and Kumar Murugesan Raja. "A data quarantine model to secure data in edge computing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 3309–19. https://doi.org/10.11591/ijece.v12i3.pp3309-3319.

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Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data

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30

J, Ujwala Rekha, and Shahu Chatrapati K. "Competitive equilibrium based personal data market model." Indian Journal of Science and Technology 13, no. 13 (2020): 1450–57. https://doi.org/10.17485/IJST/v13i13.267.

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Abstract <strong>Background/Objectives:</strong> In a digital economy, with the increasing commercial value, personal data is viewed as a commodity to be bought and sold. Data owners expect an appropriate compensation for trading off their privacy depending on how they value their privacy. Simultaneously, data consumers want to maximize their utility which is dependent on the value derived from the data. Consequently, a data market model that optimally recompenses data owners and maximizes the profits of data consumers is required. <strong>Methods:</strong> In this study, a data

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31

Kim, Dae-Hwan, and Chan-Ki Jeong. "Software Model Integration Using Metadata Model Based on Linked Data." Journal of the Korea society of IT services 12, no. 3 (2013): 311–21. http://dx.doi.org/10.9716/kits.2013.12.3.311.

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32

Hejazi, Aylin, Neda Abdolvand, and Saeedeh Rajaee Harandi. "Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area." International Journal of Trade, Economics and Finance 8, no. 2 (2017): 102–8. http://dx.doi.org/10.18178/ijtef.2017.8.2.547.

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33

Rorimpandey, G. C., F. I. Sangkop, V. P. Rantung, J. P. Zwart, O. E. S. Liando, and A. Mewengkang. "Data Model Performance in Data Warehousing." IOP Conference Series: Materials Science and Engineering 306 (February 2018): 012044. http://dx.doi.org/10.1088/1757-899x/306/1/012044.

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34

Lu, James J. "A Data Model for Data Integration." Electronic Notes in Theoretical Computer Science 150, no. 2 (2006): 3–19. http://dx.doi.org/10.1016/j.entcs.2005.11.031.

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35

Straub, Jeremy. "Application of Model-Based Data Transmission Techniques to Gravitational Model Data." Journal of Data Analysis and Information Processing 01, no. 03 (2013): 46–57. http://dx.doi.org/10.4236/jdaip.2013.13007.

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36

Nativi, Stefano, John Caron, Ben Domenico, and Lorenzo Bigagli. "Unidata’s Common Data Model mapping to the ISO 19123 Data Model." Earth Science Informatics 1, no. 2 (2008): 59–78. http://dx.doi.org/10.1007/s12145-008-0011-6.

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37

Helskyaho, Heli, Laura Ruotsalainen, and Tomi Männistö. "Defining Data Model Quality Metrics for Data Vault 2.0 Model Evaluation." Inventions 9, no. 1 (2024): 21. http://dx.doi.org/10.3390/inventions9010021.

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Designing a database is a crucial step in providing businesses with high-quality data for decision making. The quality of a data model is the key to the quality of its data. Evaluating the quality of a data model is a complex and time-consuming task. Having suitable metrics for evaluating the quality of a data model is an essential requirement for automating the design process of a data model. While there are metrics available for evaluating data warehouse data models to some degree, there is a distinct lack of metrics specifically designed to assess how well a data model conforms to the rules

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K., Dharani* &. Dr. G. Abel Thangaraja**. "BIG DATA PREPROCESSING USING ENHANCED DATA QUALITY RULES DISCOVERY MODEL (EDQRM)." International Journal of Engineering Research and Modern Education (IJERME) 8, no. 2 (2023): 33–41. https://doi.org/10.5281/zenodo.8428545.

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In the Big Data Era, data is the center for any governmental, institutional, and private organization. Endeavors were equipped towards extricating profoundly important bits of knowledge that can't occur assuming data is of low quality. Hence, data quality (DQ) is considered as a vital component in big data processing. In this stage, bad quality data isn't entered to the Big Data value chain. This paper, proposed the Enhanced data quality Rules discovery model (EDQRM) for assessment of quality and Big Data pre-processing. EDQRM discovery model to improve and precisely focus on the pre-p

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Dong, Xiuze, Yunchuan Guo, Fenghua Li, Liju Dong, and Arshad Khan. "Combination Model of Heterogeneous Data for Security Measurement." JUCS - Journal of Universal Computer Science 25, no. (3) (2019): 270–81. https://doi.org/10.3217/jucs-025-03-0270.

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Measuring security is a core step for guaranteeing security of network and information systems. Due to massiveness and heterogeneity of measurement data, it is difficult to classify and combine them on demand. In this paper, considering implication relationship of metrics, we propose a combination model and combination policy for security measurement. Several examples demonstrate the effectiveness of our model.

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40

Nikiforova, Anastasija, Janis Bicevskis, Zane Bicevska, and Ivo Oditis. "User-Oriented Approach to Data Quality Evaluation." JUCS - Journal of Universal Computer Science 26, no. (1) (2020): 107–26. https://doi.org/10.3897/jucs.2020.007.

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The paper proposes a new data object-driven approach to data quality evaluation. It consists of three main components: (1) a data object, (2) data quality requirements, and (3) data quality evaluation process. As data quality is of relative nature, the data object and quality requirements are (a) use-case dependent and (b) defined by the user in accordance with his needs. All three components of the presented data quality model are described using graphical Domain Specific Languages (DSLs). In accordance with Model-Driven Architecture (MDA), the data quality model is built in two steps: (1) cr

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41

Łacheciński, Sebastian. "MODELING OF TEMPORAL DATA IN THE RELATIONAL DATA MODEL." INFORMATYKA EKONOMICZNA, no. 46 (2017): 90–107. http://dx.doi.org/10.15611/ie.2017.4.08.

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42

Wu, Jian, and Liugen Xue. "Model Detection for Additive Models with Longitudinal Data." Open Journal of Statistics 04, no. 10 (2014): 868–78. http://dx.doi.org/10.4236/ojs.2014.410082.

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43

Jackson, Aleczander, Barrion Palmer, Daniel Cobb, and Joseph McCreless. "Model of Models Methodology: Reuse Your Architectural Data." INCOSE International Symposium 31, no. 1 (2021): 1303–18. http://dx.doi.org/10.1002/j.2334-5837.2021.00902.x.

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44

Moheimani, S. O. Reza. "Model Correction for Sampled-Data Models of Structures." Journal of Guidance, Control, and Dynamics 24, no. 3 (2001): 634–37. http://dx.doi.org/10.2514/2.4760.

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45

Jensen, Christian S., Michael D. Soo, and Richard T. Snodgrass. "Unifying temporal data models via a conceptual model." Information Systems 19, no. 7 (1994): 513–47. http://dx.doi.org/10.1016/0306-4379(94)90013-2.

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46

Deokule, Shruti. "Disease Prediction Model Using Multi-Modal Data Fusion." International Journal of Scientific Research and Engineering Trends 11, no. 2 (2025): 2226–30. https://doi.org/10.61137/ijsret.vol.11.issue2.351.

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47

Leo Alexander T, Leo Alexander T., Pari Dayal L. Pari Dayal L, Valarmathi S. Valarmathi S, Ponnuraja C. Ponnuraja C, and Venkatesan P. Venkatesan P. "Bayesian Frailty Model for Time to Event Breast Cancer Data." Indian Journal of Applied Research 4, no. 2 (2011): 8–12. http://dx.doi.org/10.15373/2249555x/feb2014/184.

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48

Ganske, Anette, Daniel Heydebreck, Heinke Höck, Angelina Kraft, Johannes Quaas, and Amandine Kaiser. "A short guide to increase FAIRness of atmospheric model data." Meteorologische Zeitschrift 29, no. 6 (2020): 483–91. http://dx.doi.org/10.1127/metz/2020/1042.

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Kurz, Christian, and Volker Grewe. "Lightning and thunderstorms, Part I: Observational data and model results." Meteorologische Zeitschrift 11, no. 6 (2002): 379–93. http://dx.doi.org/10.1127/0941-2948/2002/0011-0379.

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Menachem, Domb, Joshi Sujata, and Pandey Roshan. "Risk Mitigation Model for Data Loss: A Case Study Approach." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (2019): 440–47. http://dx.doi.org/10.5373/jardcs/v11/20192590.

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