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Статті в журналах з теми "Real data model"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

AbdElHamid, Amr, Peng Zong, and Bassant Abdelhamid. "Advanced UAV Hybrid Simulator Model Based-on Dynamic Real Weather Data." International Journal of Modeling and Optimization 5, no. 4 (2015): 246–56. http://dx.doi.org/10.7763/ijmo.2015.v5.470.

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2

Mueller, Conrad S. M. "A New Computational Model for Real Gains in Big Data Processing Power." Advances in Cyber-Physical Systems 2, no. 1 (March 28, 2017): 11–21. http://dx.doi.org/10.23939/acps2017.01.011.

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3

Lasič, S. "Geyser model with real-time data collection." European Journal of Physics 27, no. 4 (June 19, 2006): 995–1005. http://dx.doi.org/10.1088/0143-0807/27/4/031.

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4

Charpentier, Philippe. "The LHCb Computing Model and Real Data." Journal of Physics: Conference Series 331, no. 7 (December 23, 2011): 072008. http://dx.doi.org/10.1088/1742-6596/331/7/072008.

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5

Artuso, Paola, Rupert Gammon, Fabio Orecchini, and Simon J. Watson. "Alkaline electrolysers: Model and real data analysis." International Journal of Hydrogen Energy 36, no. 13 (July 2011): 7956–62. http://dx.doi.org/10.1016/j.ijhydene.2011.01.094.

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6

Naik, Kevin, and Anton Ianakiev. "Heat demand prediction: A real-life data model vs simulated data model comparison." Energy Reports 7 (October 2021): 380–88. http://dx.doi.org/10.1016/j.egyr.2021.08.093.

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7

Yoshikawa, Kogo, and Gen Nakamura. "Model Independent MRE Data Analysis." Computational and Mathematical Methods in Medicine 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/912920.

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Анотація:

For the diagnosing modality called MRE (magnetic resonance elastography), the displacement vector of a wave propagating in a human tissue can be measured. The average of the local wavelength from this measured data could be an index for the diagnosing, because the local wave length becomes larger when the tissue is stiffer. By assuming that the local form of the wave is given approximately as multiple complex plane waves, we identify the real part of the complex linear phase of the strongest plane wave of this multiple complex plane waves, by first applying the FBI transform (Fourier-Bros-Iagolnitzer transform) with an appropriate size of Gaussian window and then taking the maximum of the modulus of the transform with respect to the Fourier variable. The real part of the linear phase is nothing but the real inner product of the wave vector and the position vector. Similarly the imaginary part of the linear phase describes the attenuation of the wave and it is given as a real inner product of a real vector and the position vector. This vector can also be recovered by our method. We also apply these methods to design some denoising and filtering for noisy MRE data.

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8

Dutta, Hiren. "Graph Based Data Governance Model for Real Time Data Ingestion." International Journal of Information Technology and Computer Science 8, no. 10 (October 8, 2016): 56–62. http://dx.doi.org/10.5815/ijitcs.2016.10.07.

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9

Srisaila, A., D. Rajani, M. V. D. N. S. Madhavi, G. Jaya Lakshmi, K. Amarendra, and Narasimha Rao Dasari. "An Improved Data Generalization Model for Real-Time Data Analysis." Scientific Programming 2022 (August 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/4118371.

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This research proposes a maximum likelihood-Weibull distribution (WD) model for the generalized data distribution family. The distribution function of the anticipated maximum likelihood-Weibull distribution is defined where the statistical properties are derived. The data distribution is capable of modelling monotonically decreasing, increasing, and constant hazard rates. The proposed maximum likelihood-Weibull distribution is used for evaluated these parameters. The experimentation is done to evaluate the potential of the maximum likelihood-Weibull distribution estimated. Here, the online available dataset is adopted for computing the anticipated maximum likelihood-Weibull distribution performance. The outcomes show that the anticipated model is well-suited for computation and compared with other distributions as it possesses maximal and least value of some statistical criteria.

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10

Dutta, Hiren. "Graph based data governance model for real time data ingestion." CSI Transactions on ICT 3, no. 2-4 (December 2015): 119–25. http://dx.doi.org/10.1007/s40012-016-0079-y.

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11

Stojanovic, Vladica, and Biljana Popovic. "Estimation in real data set by Split-ARCH model." Filomat 21, no. 2 (2007): 133–52. http://dx.doi.org/10.2298/fil0702133s.

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Famous models of conditional heteroscedasticity describe various effects of behavior of the financial markets. In this paper, we investigate the related model, called Split-ARCH, in some of its stochastic aspects, as the necessary and sufficient conditions of the strong stationarity and the estimation procedure. The basic asymptotic properties of those estimates are described, too. The most important segment of our work is dedicated to the practical issue of Split-ARCH model in analysis of the dynamics of the real data. We compared the Split-ARCH with standard models of ARCH type and showed that it was better stochastic model for the explanation of the world market prices of some precious metals. .

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12

Colombo, Rinaldo M., and Francesca Marcellini. "A traffic model aware of real time data." Mathematical Models and Methods in Applied Sciences 26, no. 03 (February 10, 2016): 445–67. http://dx.doi.org/10.1142/s0218202516500081.

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Nowadays, traffic monitoring systems have access to real time data, e.g. through GPS devices. We propose a new traffic model able to take into account these data and, hence, able to describe the effects of unpredictable accidents. The well-posedness of this model is proved and numerical integrations show qualitative features of the resulting solutions. As a further motivation for the use of real time data, we show that the inverse problem for the Lighthill–Whitham and Richards (LWR) model is ill-posed.

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13

Chabaniuk, V., and O. Dyshlyk. "National spatial data infrastructure (nsdi) of Ukraine: what are its actual, feasible and simultaneously "correct" models?" Zemleustrìj, kadastr ì monìtorìng zemelʹ, no. 3 (August 28, 2021): 11. http://dx.doi.org/10.31548/zemleustriy2021.03.11.

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The actual, feasible and simultaneously "correct" models of digital NSDI of Ukraine are considered in the work. A model of the existed digital NSDI system of Ukraine is named “actual”. This model already differs from the model defined by the [1]. As the latter is unlikely to be implemented in the near future, the issue of the digital feasible NSDI model of Ukraine in the next five years, which would take into account the actual model, is especially acute. In addition to feasibility, such a model must also be "correct", what is proposed in the article. The correct is called a model, the truth of which can be established by inductive or deductive reasoning. To do this, the correct model must be formalized enough so that everyone can verify the authors’ reasoning independently. Understanding both actual and correct models of NSDI of Ukraine will help to properly organize and develop actual Spatial Infrastructure Activities (SpIA) in Ukraine, including the real[1] implementation of the [1]. Although the results of the article call into question its feasibility and substantiate an alternative viewpoint on the automation problem of NGDI/NSDI/SpIA. However, we are convinced that it is still possible to change the alternative viewpoint to a cooperative one, if by means of by-laws the models of NGDI (Law), NSDI (article) and, finally, SpIA are agreed upon To prove the "correctness" of the feasible NSDI model, the theory of Relational cartography and its two main methods are used: Conceptual Frameworks and Solution Frameworks. In addition, the correspondence between Relational cartography and Model-Based Engineering is used. Key words: NSDI; product model; process model; actual, feasible and «correct» model. [1] Real. 1. Which exists in reality, true. Is used with: reality, life, existence, conditions, circumstances, fact, danger, force, wages, income. 2. One that can be implemented, executed: a real plan, a real program, a real task, a real deadline. 3. Which is based on taking into account and assessing the real conditions of reality: a real approach, a real view, a real policy.- accessed 2021-feb-14, http://slovopedia.org.ua/32/53408/32016.html (Ukrainian).

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14

Cheng, Jing, Lloyd J. Edwards, Mildred M. Maldonado-Molina, Kelli A. Komro, and Keith E. Muller. "Real longitudinal data analysis for real people: Building a good enough mixed model." Statistics in Medicine 29, no. 4 (December 10, 2009): 504–20. http://dx.doi.org/10.1002/sim.3775.

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15

Chakraborty, Aditya, and Dr Chris P. Tsokos. "A Real Data-Driven Analytical Model to Predict Happiness." Scholars Journal of Physics, Mathematics and Statistics 8, no. 3 (March 9, 2021): 45–61. http://dx.doi.org/10.36347/sjpms.2021.v08i03.001.

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16

Choi. "Comparison of Data-based Real-Time Flood Forecasting Model." Journal of the Korean Society of Civil Engineers 33, no. 5 (2013): 1809. http://dx.doi.org/10.12652/ksce.2013.33.5.1809.

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17

Kodikara, K. A. T. L., T. H. T. Chan, T. Nguyen, and D. P. Thambiratnam. "Model updating of real structures with ambient vibration data." Journal of Civil Structural Health Monitoring 6, no. 3 (June 3, 2016): 329–41. http://dx.doi.org/10.1007/s13349-016-0178-3.

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18

Gatherer, A., and T. H. Y. Meng. "Fitting real data to a pulse position jittered model." IEEE Transactions on Magnetics 26, no. 5 (1990): 2143–45. http://dx.doi.org/10.1109/20.104648.

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19

Babajani-Feremi, Abbas, Hamid Soltanian-Zadeh, and John E. Moran. "Integrated MEG/fMRI Model Validated Using Real Auditory Data." Brain Topography 21, no. 1 (May 14, 2008): 61–74. http://dx.doi.org/10.1007/s10548-008-0056-3.

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20

Pasha, G. R., Muhammad Aslam, and Muhammad Abdullah. "Dynamic Panel Data Model for Investment, Real Value and Capital Stock Data." Pakistan Journal of Statistics and Operation Research 3, no. 1 (January 1, 2007): 13. http://dx.doi.org/10.18187/pjsor.v3i1.71.

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21

Azizi, Ilia, and Iegor Rudnytskyi. "Improving Real Estate Rental Estimations with Visual Data." Big Data and Cognitive Computing 6, no. 3 (September 9, 2022): 96. http://dx.doi.org/10.3390/bdcc6030096.

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Анотація:

Multi-modal data are widely available for online real estate listings. Announcements can contain various forms of data, including visual data and unstructured textual descriptions. Nonetheless, many traditional real estate pricing models rely solely on well-structured tabular features. This work investigates whether it is possible to improve the performance of the pricing model using additional unstructured data, namely images of the property and satellite images. We compare four models based on the type of input data they use: (1) tabular data only, (2) tabular data and property images, (3) tabular data and satellite images, and (4) tabular data and a combination of property and satellite images. In a supervised context, the branches of dedicated neural networks for each data type are fused (concatenated) to predict log rental prices. The novel dataset devised for the study (SRED) consists of 11,105 flat rentals advertised over the internet in Switzerland. The results reveal that using all three sources of data generally outperforms machine learning models built on only tabular information. The findings pave the way for further research on integrating other non-structured inputs, for instance, the textual descriptions of properties.

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22

Alizadeh Noughabi, Hadi. "Testing the Validity of Laplace Model Against Symmetric Models, Using Transformed Data." Statistics, Optimization & Information Computing 10, no. 4 (August 17, 2021): 1162–67. http://dx.doi.org/10.19139/soic-2310-5070-1030.

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In this paper, we first present three characterizations of Laplace distribution and then we introduce a goodness of fit test for Laplace distribution against symmetric distributions, based on one of the transformations. The power of the proposed test under various alternatives is compared with that of the existing tests, by simulation. To show the behavior of the proposed test in real cases, two real examples are presented.

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23

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 (March 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 three layers namely Sensor, Fog and cloud to solve the aforementioned issue. Sensors generate patient data and that are managed and processed by Fog and cloud layers. Tasks are scheduled using a Weighted Fog Priority Job Scheduling algorithm (WFPJS) and fog nodes are allocated based on Priority based Virtual Machine Classification Algorithm (PVCA). The performance of this model is validated with static scheduling techniques with variable patient counts and network configurations. The proposed FMDM with WFPJS reduces response time, total execution cost, network usage, network latency, computational latency and energy consumption.</p> <p> </p>

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24

Mutasher, Watheq Ghanim, and Abbas Fadhil Aljuboori. "Real Time Big Data Sentiment Analysis and Classification of Facebook." Webology 19, no. 1 (January 20, 2022): 1112–27. http://dx.doi.org/10.14704/web/v19i1/web19076.

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Many peoples use Facebook to connect and share their views on various issues, with the majority of user-generated content consisting of textual information. Since there is so much actual data from people who are posting messages on their situation in real time thoughts on a range of subjects in everyday life, the collection and analysis of these data, which may well be helpful for political decision or public opinion monitoring, is a worthwhile research project. Therefore, in this paper doing to analyze for public text post on Facebook stream in real time through environment Hadoop ecosystem by using apache spark with NLTK python. The post or feeds are gathered form the Facebook API in real time the data stored database used Apache spark to quick query processing the text partitions in each data nodes (machine). Also used Amazon cloud based Hadoop cluster ecosystem into processing of huge data and eliminate on-site hardware, IT support, and other operational difficulties and installation configuration Hadoop such as Hadoop distribution file system and Apache spark. By using the principle of decision dictionary, emotion analysis is used as positive, negative, or neutral and execution two algorithms in machine learning (naive bias & support vector machine) to build model predict the outcome demonstrates a high level of precision in sentiment analysis.

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25

Galmarini, S., R. Bianconi, G. de Vries, and R. Bellasio. "Real-time monitoring data for real-time multi-model validation: coupling ENSEMBLE and EURDEP." Journal of Environmental Radioactivity 99, no. 8 (August 2008): 1233–41. http://dx.doi.org/10.1016/j.jenvrad.2008.02.006.

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26

Catley, Christina, Kathy Smith, Carolyn McGregor, Andrew James, and J. Mikael Eklund. "A Framework for Multidimensional Real-Time Data Analysis." International Journal of Computational Models and Algorithms in Medicine 2, no. 1 (January 2011): 16–37. http://dx.doi.org/10.4018/jcmam.2011010102.

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In this paper, the authors present a framework to support multidimensional analysis of real-time physiological data streams and clinical data. The clinical context for the case study demonstration is neonatal intensive care, focusing specifically on the detection of episodes of central apnoea, a clinically significant problem. The model accounts for the multidimensional and real-time nature of apnoea of prematurity and the associated clinical rules. The framework demonstration includes: 1) defining rules that quantify concurrent behaviours between multiple synchronous data streams and asynchronous data values; 2) designing UML models to define present practice event processing for episodes of apnoea; 3) translating the model in SPADE to enable the deployment within the real-time processing layer of the Artemis platform, which utilizes IBM’s InfoSphere Streams; 4) demonstrating knowledge discovery with simple and complex temporal abstractions of the data streams; and 5) presenting results for early detection of episodes of apnoea across multiple physiological data streams.

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27

Vanrolleghem, Peter A., and Karel J. Keesman. "Identification of Biodegradation models under model and data uncertainty." Water Science and Technology 33, no. 2 (January 1, 1996): 91–105. http://dx.doi.org/10.2166/wst.1996.0040.

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In this paper a number of nonlinear parameter estimation methods are evaluated with respect to their ability to identify biodegradation models from “real-world” data. Important aspects are then the sensitivity to local minima, rate of convergence, required prior knowledge and direct or indirect availability of parameter estimates uncertainty. Furthermore, it is important whether a method is robust against invalid assumptions. In addition to the final parameter values, covariance and correlation matrices, confidence intervals and residual sequences are presented to obtain information about the validity of the models and noise assumptions. Finally, recommendations on the method's applicability range are provided.

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28

Rezaei-Yazdi, Ali, and Christopher Buckingham. "Introducing a Pilot Data Collection Model for Real-time Evaluation of Data Redundancy." Procedia Computer Science 96 (2016): 577–86. http://dx.doi.org/10.1016/j.procs.2016.08.237.

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29

Rankin, Debbie, Michaela Black, Raymond Bond, Jonathan Wallace, Maurice Mulvenna, and Gorka Epelde. "Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing." JMIR Medical Informatics 8, no. 7 (July 20, 2020): e18910. http://dx.doi.org/10.2196/18910.

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Background The exploitation of synthetic data in health care is at an early stage. Synthetic data could unlock the potential within health care datasets that are too sensitive for release. Several synthetic data generators have been developed to date; however, studies evaluating their efficacy and generalizability are scarce. Objective This work sets out to understand the difference in performance of supervised machine learning models trained on synthetic data compared with those trained on real data. Methods A total of 19 open health datasets were selected for experimental work. Synthetic data were generated using three synthetic data generators that apply classification and regression trees, parametric, and Bayesian network approaches. Real and synthetic data were used (separately) to train five supervised machine learning models: stochastic gradient descent, decision tree, k-nearest neighbors, random forest, and support vector machine. Models were tested only on real data to determine whether a model developed by training on synthetic data can used to accurately classify new, real examples. The impact of statistical disclosure control on model performance was also assessed. Results A total of 92% of models trained on synthetic data have lower accuracy than those trained on real data. Tree-based models trained on synthetic data have deviations in accuracy from models trained on real data of 0.177 (18%) to 0.193 (19%), while other models have lower deviations of 0.058 (6%) to 0.072 (7%). The winning classifier when trained and tested on real data versus models trained on synthetic data and tested on real data is the same in 26% (5/19) of cases for classification and regression tree and parametric synthetic data and in 21% (4/19) of cases for Bayesian network-generated synthetic data. Tree-based models perform best with real data and are the winning classifier in 95% (18/19) of cases. This is not the case for models trained on synthetic data. When tree-based models are not considered, the winning classifier for real and synthetic data is matched in 74% (14/19), 53% (10/19), and 68% (13/19) of cases for classification and regression tree, parametric, and Bayesian network synthetic data, respectively. Statistical disclosure control methods did not have a notable impact on data utility. Conclusions The results of this study are promising with small decreases in accuracy observed in models trained with synthetic data compared with models trained with real data, where both are tested on real data. Such deviations are expected and manageable. Tree-based classifiers have some sensitivity to synthetic data, and the underlying cause requires further investigation. This study highlights the potential of synthetic data and the need for further evaluation of their robustness. Synthetic data must ensure individual privacy and data utility are preserved in order to instill confidence in health care departments when using such data to inform policy decision-making.

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30

Shobirin, Kheri Arionadi, Adi Panca Saputra Iskandar, and Ida Bagus Alit Swamardika. "Data Warehouse Schemas using Multidimensional Data Model for Retail." International Journal of Engineering and Emerging Technology 2, no. 1 (September 23, 2017): 84. http://dx.doi.org/10.24843/ijeet.2017.v02.i01.p17.

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A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

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31

Jafari, Ehsan, Behzad Moshiri, Karim Salahshoor, and Amin Ramezani. "Real Data Testing of Model Parameter Estimation Methods for Macroscopic Traffic Flow Model." IFAC Proceedings Volumes 43, no. 16 (2010): 419–24. http://dx.doi.org/10.3182/20100906-3-it-2019.00073.

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32

Zheng, Chenwei, Deshawn Murray Coombs, and Benjamin Akih-Kumgeh. "Real Gas Model Parameters for High-Density Combustion from Chemical Kinetic Model Data." ACS Omega 4, no. 2 (February 12, 2019): 3074–82. http://dx.doi.org/10.1021/acsomega.8b03150.

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33

Zhengyi Yang, and Shilong Wang. "Model and Performance Analysis of Real-time Monitoring Data Processing." International Journal of Digital Content Technology and its Applications 6, no. 17 (September 30, 2012): 595–602. http://dx.doi.org/10.4156/jdcta.vol6.issue17.65.

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34

Rudloff, Christian, Robert Schönauer, and Martin Fellendorf. "Comparing Calibrated Shared Space Simulation Model with Real-Life Data." Transportation Research Record: Journal of the Transportation Research Board 2390, no. 1 (January 2013): 44–52. http://dx.doi.org/10.3141/2390-05.

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35

Venna, Siva R., Amirhossein Tavanaei, Raju N. Gottumukkala, Vijay V. Raghavan, Anthony S. Maida, and Stephen Nichols. "A Novel Data-Driven Model for Real-Time Influenza Forecasting." IEEE Access 7 (2019): 7691–701. http://dx.doi.org/10.1109/access.2018.2888585.

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36

FUNG, S. W. C., C. O. TONG, and S. C. WONG. "Validation of a Conventional Metro Network Model Using Real Data." Journal of Intelligent Transportation Systems 9, no. 2 (April 2005): 69–79. http://dx.doi.org/10.1080/15472450590934624.

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37

Nicholson, Hugh, Per Gunnar Folstad, and Terje A. Pedersen. "Real-time data transfer to 3D model—maximizing wellbore value." Leading Edge 23, no. 6 (June 2004): 592–97. http://dx.doi.org/10.1190/1.1766235.

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38

Liu, Kai, Sivanagaraja Tatinati, and Andy W. H. Khong. "Context-Based Data Model for Effective Real-Time Learning Analytics." IEEE Transactions on Learning Technologies 13, no. 4 (October 1, 2020): 790–803. http://dx.doi.org/10.1109/tlt.2020.3027441.

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39

Faccio, Maurizio, Alessandro Persona, and Giorgia Zanin. "Waste collection multi objective model with real time traceability data." Waste Management 31, no. 12 (December 2011): 2391–405. http://dx.doi.org/10.1016/j.wasman.2011.07.005.

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40

Chong, Kil To, and Sung Goo Yoo. "Neural network prediction model for a real-time data transmission." Neural Computing and Applications 15, no. 3-4 (March 30, 2006): 373–82. http://dx.doi.org/10.1007/s00521-006-0042-1.

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41

Thornton, P. K., and J. W. Hansen. "A note on regressing real-world data on model output." Agricultural Systems 50, no. 4 (January 1996): 411–14. http://dx.doi.org/10.1016/0308-521x(95)00012-t.

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42

Hartmann, Andreas, Oleg Akimov, Stephen Morris, and Christian Fulda. "Improving Real-Time Image-Data Quality With A Telemetry Model." SPE Drilling & Completion 27, no. 03 (September 1, 2012): 383–92. http://dx.doi.org/10.2118/142420-pa.

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43

Wisniewski, Mariusz, Zeeshan A. Rana, and Ivan Petrunin. "Drone Model Classification Using Convolutional Neural Network Trained on Synthetic Data." Journal of Imaging 8, no. 8 (August 12, 2022): 218. http://dx.doi.org/10.3390/jimaging8080218.

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We present a convolutional neural network (CNN) that identifies drone models in real-life videos. The neural network is trained on synthetic images and tested on a real-life dataset of drone videos. To create the training and validation datasets, we show a method of generating synthetic drone images. Domain randomization is used to vary the simulation parameters such as model textures, background images, and orientation. Three common drone models are classified: DJI Phantom, DJI Mavic, and DJI Inspire. To test the performance of the neural network model, Anti-UAV, a real-life dataset of flying drones is used. The proposed method reduces the time-cost associated with manually labelling drones, and we prove that it is transferable to real-life videos. The CNN achieves an overall accuracy of 92.4%, a precision of 88.8%, a recall of 88.6%, and an f1 score of 88.7% when tested on the real-life dataset.

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44

Shah, Neil J., Nadia Bahadur, Lauren Esposito, Andrew Niederhausern, Chelsea Nichols, Anjali Pillai, Fenil Gandhi, et al. "A comprehensive Memorial Sloan Kettering Cancer Center real-world data model: Core clinical data elements." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e18755-e18755. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e18755.

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e18755 Background: The 2016 21st Century Cures Act supports the use of Real-World Data (RWD) for regulatory decision/approval. Due to technological advances, a vast amount of health-related data are now available, but most are not standardized nor readily useable for research. Also, currently available standardized RWD models are not applicable across cancer types or oncology specialties (surgery, medical oncology, radiation oncology, pathology, radiology, etc.). To address these deficiencies Memorial Sloan Kettering Cancer Center (MSKCC) built a comprehensive, pan-cancer, pan-specialty RWD model. Methods: The Core Clinical Data Element (CCDE) data model incorporates aspects of existing academic and biopharma data models, including PRISSMM framework, ASCO’s mCODE, and NAACCR tumor registry model. The data model encompasses 11 domains that are critical to the understanding of the patient’s cancer journey, including: demographic, comorbidities, diagnosis, pathology, imaging, genomics, cancer surgeries, radiation oncology treatments, medical oncology treatments, cancer status/progression, and additional health information. To align with current standards, we are using ICD-10, ICDO3, CTACE V5.0, HL7, SNOMED and LOINC code sets. Further, this adaptable model allows for 5-10 disease specific elements to accommodate for disease heterogenicity and capture the differences among cancer types. Results: The CCDE database includes 1,126 of total data elements. MSKCC has 52,704 patients with MSK-IMPACT (Next-Generation sequencing platform with 505 genes panel) testing of which, we have identified 1,132 bladder cancer patients with at-least one year of cancer care follow-up for the initial curation cohort. Patients were identified as having an OncoTree bladder tumor type code that is assigned by a pathologist who attests the diagnosis by reviewing results from clinical tests on tumor specimens. To the date, 641 patients including 46,415 curated forms have been curated (Table). Conclusions: The comprehensive MSKCC’s CCDE data model standardizes the common and critical pan-cancer and pan-specialty elements for RWD. The dataset resulting from this curation efforts will provide robust structured and unified genomic and phenomic data across tumor types for future research enabling greater collaboration across various cancer types as well as oncology specialties.[Table: see text]

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45

Brown, Samantha, Jasme Lee, Anjali Pillai, Fenil Gandhi, Nadia Bahadur, Laura Barton, Kimberly Chan, et al. "Real-time data quality assurance analysis for real-world, pan-cancer data." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e18775-e18775. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e18775.

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e18775 Background: The production of high-quality real-world data requires comprehensive and meticulous data quality assurance (QA) methods to guarantee that adequate standards of accuracy, completeness, and consistency are met. Memorial Sloan Kettering Cancer Center (MSKCC) synthesizes manually curated Electronic Health Record (EHR) data to collect and harmonize the fundamental data elements across all cancer types. Centralized real-time analysis of curated data quality can allow for rigorous review to identify areas of strength and opportunities for improvement in the curation process. Methods: MSKCC built the Core Clinical Data Element (CCDE) data model, which encompasses aspects of PRISSMM, ASCO’s mCODE, and NAACCR tumor registry frameworks, to capture standardized real-world, pan-cancer, pan-specialty data across 11 modules, including cancer genomics, imaging, pathology, surgery, and radiation. A key component within the QA process is source data verification (SDV), the comparison of curated data against source documents to identify inconsistencies. Any discrepancies detected are classified into major and minor violations. Major violations are errors or omissions on core data elements that would impact time interval calculations, such as an incorrect procedure date. Minor violations are errors or omissions on less critical data elements, such as a missing radiation therapy dose. Identifying these inconsistences allows the QA team to recognize patterns in curation errors and distinguish areas for curator retraining. Results: With limited functionality in basic standard data quality checks that exist across various data storage platforms, an interactive application was developed using the R Shiny package to access data as cases are recorded and summarize findings from SDV in real time. The app has two panels, each stratified by CCDE module. The first panel details the total number of forms curated and percentage of forms that underwent SDV, with each form representing one of the 11 modules. The other panel consists of a set of tables that summarize specific major and minor violations based on user selection of a denominator of either patients (e.g. how many patients had a violation on at least one imaging report) or forms (e.g. how many imaging reports had a violation). We will demonstrate the utility of the app and discuss benefits of real time evaluation in large-scale, real-world EHR curation efforts. Conclusions: We recommend automated, user-friendly tools to assess data quality of such efforts. With real-time analysis, the tool allows for ongoing and regular data checks, enabling clarification of directives and retraining of curators as necessary early in the curation process. As the data curation efforts expand to more cancer cohorts, the app examines data quality of each cohort to ensure consistent evaluation. This offers transparency of data quality to ensure usability in real-world data for rigorous research.

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46

Zhang, Yunjun, Tom Britton, and Xiaohua Zhou. "Monitoring real-time transmission heterogeneity from incidence data." PLOS Computational Biology 18, no. 12 (December 1, 2022): e1010078. http://dx.doi.org/10.1371/journal.pcbi.1010078.

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The transmission heterogeneity of an epidemic is associated with a complex mixture of host, pathogen and environmental factors. And it may indicate superspreading events to reduce the efficiency of population-level control measures and to sustain the epidemic over a larger scale and a longer duration. Methods have been proposed to identify significant transmission heterogeneity in historic epidemics based on several data sources, such as contact history, viral genomes and spatial information, which may not be available, and more importantly ignore the temporal trend of transmission heterogeneity. Here we attempted to establish a convenient method to estimate real-time heterogeneity over an epidemic. Within the branching process framework, we introduced an instant-individualheterogenous infectiousness model to jointly characterize the variation in infectiousness both between individuals and among different times. With this model, we could simultaneously estimate the transmission heterogeneity and the reproduction number from incidence time series. We validated the model with data of both simulated and real outbreaks. Our estimates of the overall and real-time heterogeneities of the six epidemics were consistent with those presented in the literature. Additionally, our model is robust to the ubiquitous bias of under-reporting and misspecification of serial interval. By analyzing recent data from South Africa, we found evidence that the Omicron might be of more significant transmission heterogeneity than Delta. Our model based on incidence data was proved to be reliable in estimating the real-time transmission heterogeneity.

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47

Ding, Yulin, Hui Lin, and Rongrong Li. "CHANGE SEMANTIC CONSTRAINED ONLINE DATA CLEANING METHOD FOR REAL-TIME OBSERVATIONAL DATA STREAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 177–83. http://dx.doi.org/10.5194/isprs-archives-xli-b2-177-2016.

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Recent breakthroughs in sensor networks have made it possible to collect and assemble increasing amounts of real-time observational data by observing dynamic phenomena at previously impossible time and space scales. Real-time observational data streams present potentially profound opportunities for real-time applications in disaster mitigation and emergency response, by providing accurate and timeliness estimates of environment’s status. However, the data are always subject to inevitable anomalies (including errors and anomalous changes/events) caused by various effects produced by the environment they are monitoring. The “big but dirty” real-time observational data streams can rarely achieve their full potential in the following real-time models or applications due to the low data quality. Therefore, timely and meaningful online data cleaning is a necessary pre-requisite step to ensure the quality, reliability, and timeliness of the real-time observational data. <br><br> In general, a straightforward streaming data cleaning approach, is to define various types of models/classifiers representing normal behavior of sensor data streams and then declare any deviation from this model as normal or erroneous data. The effectiveness of these models is affected by dynamic changes of deployed environments. Due to the changing nature of the complicated process being observed, real-time observational data is characterized by diversity and dynamic, showing a typical Big (Geo) Data characters. Dynamics and diversity is not only reflected in the data values, but also reflected in the complicated changing patterns of the data distributions. This means the pattern of the real-time observational data distribution is not <i>stationary or static</i> but <i>changing and dynamic</i>. After the data pattern changed, it is necessary to adapt the model over time to cope with the changing patterns of real-time data streams. Otherwise, the model will not fit the following observational data streams, which may led to large estimation error. In order to achieve the best generalization error, it is an important challenge for the data cleaning methodology to be able to characterize the behavior of data stream distributions and adaptively update a model to include new information and remove old information. However, the complicated data changing property invalidates traditional data cleaning methods, which rely on the assumption of a stationary data distribution, and drives the need for more dynamic and adaptive online data cleaning methods. <br><br> To overcome these shortcomings, this paper presents a change semantics constrained online filtering method for real-time observational data. Based on the principle that the filter parameter should vary in accordance to the data change patterns, this paper embeds semantic description, which quantitatively depicts the change patterns in the data distribution to self-adapt the filter parameter automatically. Real-time observational water level data streams of different precipitation scenarios are selected for testing. Experimental results prove that by means of this method, more accurate and reliable water level information can be available, which is prior to scientific and prompt flood assessment and decision-making.

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48

Ding, Yulin, Hui Lin, and Rongrong Li. "CHANGE SEMANTIC CONSTRAINED ONLINE DATA CLEANING METHOD FOR REAL-TIME OBSERVATIONAL DATA STREAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 177–83. http://dx.doi.org/10.5194/isprsarchives-xli-b2-177-2016.

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Анотація:

Recent breakthroughs in sensor networks have made it possible to collect and assemble increasing amounts of real-time observational data by observing dynamic phenomena at previously impossible time and space scales. Real-time observational data streams present potentially profound opportunities for real-time applications in disaster mitigation and emergency response, by providing accurate and timeliness estimates of environment’s status. However, the data are always subject to inevitable anomalies (including errors and anomalous changes/events) caused by various effects produced by the environment they are monitoring. The “big but dirty” real-time observational data streams can rarely achieve their full potential in the following real-time models or applications due to the low data quality. Therefore, timely and meaningful online data cleaning is a necessary pre-requisite step to ensure the quality, reliability, and timeliness of the real-time observational data. <br><br> In general, a straightforward streaming data cleaning approach, is to define various types of models/classifiers representing normal behavior of sensor data streams and then declare any deviation from this model as normal or erroneous data. The effectiveness of these models is affected by dynamic changes of deployed environments. Due to the changing nature of the complicated process being observed, real-time observational data is characterized by diversity and dynamic, showing a typical Big (Geo) Data characters. Dynamics and diversity is not only reflected in the data values, but also reflected in the complicated changing patterns of the data distributions. This means the pattern of the real-time observational data distribution is not <i>stationary or static</i> but <i>changing and dynamic</i>. After the data pattern changed, it is necessary to adapt the model over time to cope with the changing patterns of real-time data streams. Otherwise, the model will not fit the following observational data streams, which may led to large estimation error. In order to achieve the best generalization error, it is an important challenge for the data cleaning methodology to be able to characterize the behavior of data stream distributions and adaptively update a model to include new information and remove old information. However, the complicated data changing property invalidates traditional data cleaning methods, which rely on the assumption of a stationary data distribution, and drives the need for more dynamic and adaptive online data cleaning methods. <br><br> To overcome these shortcomings, this paper presents a change semantics constrained online filtering method for real-time observational data. Based on the principle that the filter parameter should vary in accordance to the data change patterns, this paper embeds semantic description, which quantitatively depicts the change patterns in the data distribution to self-adapt the filter parameter automatically. Real-time observational water level data streams of different precipitation scenarios are selected for testing. Experimental results prove that by means of this method, more accurate and reliable water level information can be available, which is prior to scientific and prompt flood assessment and decision-making.

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49

Abd El-Raheem, Abd El-Raheem M., Mona Hosny, and Mahmoud H. Abu-Moussa. "On Progressive Censored Competing Risks Data: Real Data Application and Simulation Study." Mathematics 9, no. 15 (July 31, 2021): 1805. http://dx.doi.org/10.3390/math9151805.

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Competing risks are frequently overlooked, and the event of interest is analyzed with conventional statistical techniques. In this article, we consider the analysis of bi-causes of failure in the context of competing risk models using the extension of the exponential distribution under progressive Type-II censoring. Maximum likelihood estimates for the unknown parameters via the expectation-maximization algorithm are obtained. Moreover, the Bayes estimates of the unknown parameters are approximated using Tierney-Kadane and MCMC techniques. Interval estimates using Bayesian and classical techniques are also considered. Two real data sets are investigated to illustrate the different estimation methods, and to compare the suggested model with Weibull distribution. Furthermore, the estimation methods are compared through a comprehensive simulation study.

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50

Fan, Ai Jing, and Ai Wan Fan. "A Supporting Framework for Real-Time Data Mining." Key Engineering Materials 439-440 (June 2010): 1499–504. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1499.

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Computers widely used in the field of real time process control, and it's very necessary to pay more attention in the field. In this article, real-time data model, real-time data mining environment and the computing characteristics are analyzed .Based on investigating the development of a general purpose methodology for real-time data mining, we propose a novel supporting framework. The framework adopts a novel dynamic data mining process model.

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