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Journal articles on the topic 'Data Quality Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

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 (April 2017): 102–8. http://dx.doi.org/10.18178/ijtef.2017.8.2.547.

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Ryu, Kyung Seok, Joo Seok Park, and Jae Hong Park. "A Data Quality Management Maturity Model." ETRI Journal 28, no. 2 (April 10, 2006): 191–204. http://dx.doi.org/10.4218/etrij.06.0105.0026.

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3

Mounyol, Roger. "A Data Model for Quality Control." IFAC Proceedings Volumes 25, no. 8 (June 1992): 181–87. http://dx.doi.org/10.1016/s1474-6670(17)54062-8.

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4

Karplus, P. A., and K. Diederichs. "Linking Crystallographic Model and Data Quality." Science 336, no. 6084 (May 24, 2012): 1030–33. http://dx.doi.org/10.1126/science.1218231.

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5

Duan, Luling, and Ninggui Duan. "Calibration model for air quality data." IOP Conference Series: Earth and Environmental Science 450 (March 24, 2020): 012005. http://dx.doi.org/10.1088/1755-1315/450/1/012005.

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6

Aljumaili, Mustafa, Ramin Karim, and Phillip Tretten. "Metadata-based data quality assessment." VINE Journal of Information and Knowledge Management Systems 46, no. 2 (May 9, 2016): 232–50. http://dx.doi.org/10.1108/vjikms-11-2015-0059.

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Purpose The purpose of this paper is to develop data quality (DQ) assessment model based on content analysis and metadata analysis. Design/methodology/approach A literature review of DQ assessment models has been conducted. A study of DQ key performances (KPIs) has been done. Finally, the proposed model has been developed and applied in a case study. Findings The results of this study shows that the metadata data have important information about DQ in a database and can be used to assess DQ to provide decision support for decision makers. Originality/value There is a lot of DQ assessment in the literature; however, metadata are not considered in these models. The model developed in this study is based on metadata in addition to the content analysis, to find a quantitative DQ assessment.

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7

Merino, Jorge, Ismael Caballero, Bibiano Rivas, Manuel Serrano, and Mario Piattini. "A Data Quality in Use model for Big Data." Future Generation Computer Systems 63 (October 2016): 123–30. http://dx.doi.org/10.1016/j.future.2015.11.024.

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8

Radulovic, Filip, Nandana Mihindukulasooriya, Raúl García-Castro, and Asunción Gómez-Pérez. "A comprehensive quality model for Linked Data." Semantic Web 9, no. 1 (November 30, 2017): 3–24. http://dx.doi.org/10.3233/sw-170267.

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9

Artamonov, Igor, Antonina Deniskina, Vladimir Filatov, and Olga Vasilyeva. "Quality management assurance using data integrity model." MATEC Web of Conferences 265 (2019): 07031. http://dx.doi.org/10.1051/matecconf/201926507031.

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High complexity of modern quality management systems (QMS) leads to the need for new tools aimed to support them and implement continuous improvement. The key process to manage the quality level is quality assurance. The paper investigates process information integrity and consistency as properties or quality assurance process. Some basic approach for organizing data integrity metrics is described. An approach for QMS requirements assurance based on SysML modelling language is proposed.

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Tang, Xiaoqing, and Hu Yun. "Data model for quality in product lifecycle." Computers in Industry 59, no. 2-3 (March 2008): 167–79. http://dx.doi.org/10.1016/j.compind.2007.06.011.

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Rogers, James R., Tiffany J. Callahan, Tian Kang, Alan Bauck, Ritu Khare, Jeffrey S. Brown, Michael G. Kahn, and Chunhua Weng. "A Data Element-Function Conceptual Model for Data Quality Checks." eGEMs (Generating Evidence & Methods to improve patient outcomes) 7, no. 1 (April 23, 2019): 17. http://dx.doi.org/10.5334/egems.289.

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12

Gosain, Anjana, and Heena. "Literature Review of Data Model Quality Metrics of Data Warehouse." Procedia Computer Science 48 (2015): 236–43. http://dx.doi.org/10.1016/j.procs.2015.04.176.

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13

Yang, Jian, Chongchong Zhao, and Chunxiao Xing. "Big Data Market Optimization Pricing Model Based on Data Quality." Complexity 2019 (April 23, 2019): 1–10. http://dx.doi.org/10.1155/2019/5964068.

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In recent years, data has become a special kind of information commodity and promoted the development of information commodity economy through distribution. With the development of big data, the data market emerged and provided convenience for data transactions. However, the issues of optimal pricing and data quality allocation in the big data market have not been fully studied yet. In this paper, we proposed a big data market pricing model based on data quality. We first analyzed the dimensional indicators that affect data quality, and a linear evaluation model was established. Then, from the perspective of data science, we analyzed the impact of quality level on big data analysis (i.e., machine learning algorithms) and defined the utility function of data quality. The experimental results in real data sets have shown the applicability of the proposed quality utility function. In addition, we formulated the profit maximization problem and gave theoretical analysis. Finally, the data market can maximize profits through the proposed model illustrated with numerical examples.

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14

Calero, Coral, Angélica Caro, and Mario Piattini. "An Applicable Data Quality Model for Web Portal Data Consumers." World Wide Web 11, no. 4 (July 30, 2008): 465–84. http://dx.doi.org/10.1007/s11280-008-0048-y.

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15

Hong, Euy-Seok. "Software Quality Classification Model using Virtual Training Data." Journal of the Korea Contents Association 8, no. 7 (July 28, 2008): 66–74. http://dx.doi.org/10.5392/jkca.2008.8.7.066.

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16

Ge, Mouzhi, and Włodzimierz Lewoniewski. "Developing the Quality Model for Collaborative Open Data." Procedia Computer Science 176 (2020): 1883–92. http://dx.doi.org/10.1016/j.procs.2020.09.228.

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17

Gosain, Anjana, Sangeeta Sabharwal, and Sushama Nagpal. "Assessment of quality of data warehouse multidimensional model." International Journal of Information Quality 2, no. 4 (2011): 344. http://dx.doi.org/10.1504/ijiq.2011.043782.

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18

Haug, Anders, Jan Stentoft Arlbjørn, and Anne Pedersen. "A classification model of ERP system data quality." Industrial Management & Data Systems 109, no. 8 (September 25, 2009): 1053–68. http://dx.doi.org/10.1108/02635570910991292.

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19

Ofner, Martin, Boris Otto, and Hubert Österle. "A Maturity Model for Enterprise Data Quality Management." Enterprise Modelling and Information Systems Architectures 8, no. 2 (October 2013): 4–24. http://dx.doi.org/10.1007/s40786-013-0002-z.

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20

Barsi, Árpad, Zsófia Kugler, Attila Juhász, György Szabó, Carlo Batini, Hussein Abdulmuttalib, Guoman Huang, and Huanfeng Shen. "Remote sensing data quality model: from data sources to lifecycle phases." International Journal of Image and Data Fusion 10, no. 4 (June 23, 2019): 280–99. http://dx.doi.org/10.1080/19479832.2019.1625977.

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21

Mourad, M., J. L. Bertrand-Krajewski, and G. Chebbo. "Stormwater quality models: sensitivity to calibration data." Water Science and Technology 52, no. 5 (September 1, 2005): 61–68. http://dx.doi.org/10.2166/wst.2005.0110.

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Stormwater quality modelling is a useful tool in sewer systems management. Available models range from simple to detailed complex ones. The models need local data to be calibrated. In practice, calibration data are rather lacking. Only few measured events are commonly used. In this paper, the effect of the number and the variability of calibration data on models of various levels of complexity are investigated. The study is carried out on “Le Marais” catchment for suspended solids where 40 reliable measured events and good knowledge of the sewer system are available. The method used is based on resampling subsets of measured events among the 40 available ones. Three types of models were calibrated using subsets of events of different sizes and characteristics resampled among the 40 available ones. For each calibration, the model was validated against the remaining events to stand upon the quality of the model. It was found that the models are quite sensitive to calibration data, a problem neglected in practical studies. The use of more complex models does not necessarily improve modelling results since more problems and error sources are to be expected. The findings are specific to “Le Marais” catchment and the models used.

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22

Fei, Songlin, and Feng Yu. "Quality of presence data determines species distribution model performance: a novel index to evaluate data quality." Landscape Ecology 31, no. 1 (September 11, 2015): 31–42. http://dx.doi.org/10.1007/s10980-015-0272-7.

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23

Jarwar, Muhammad Aslam, and Ilyoung Chong. "Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application." Applied Sciences 10, no. 6 (March 23, 2020): 2181. http://dx.doi.org/10.3390/app10062181.

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Due to the convergence of advanced technologies such as the Internet of Things, Artificial Intelligence, and Big Data, a healthcare platform accumulates data in a huge quantity from several heterogeneous sources. The adequate usage of this data may increase the impact of and improve the healthcare service quality; however, the quality of the data may be questionable. Assessing the quality of the data for the task in hand may reduce the associated risks, and increase the confidence of the data usability. To overcome the aforementioned challenges, this paper presents the web objects based contextual data quality assessment model with enhanced classification metric parameters. A semantic ontology of virtual objects, composite virtual objects, and services is also proposed for the parameterization of contextual data quality assessment of web objects data. The novelty of this article is the provision of contextual data quality assessment mechanisms at the data acquisition, assessment, and service level for the web objects enabled semantic data applications. To evaluate the proposed data quality assessment mechanism, web objects enabled affective stress and teens’ mood care semantic data applications are designed, and a deep data quality learning model is developed. The findings of the proposed approach reveal that, once a data quality assessment model is trained on web objects enabled healthcare semantic data, it could be used to classify the incoming data quality in various contextual data quality metric parameters. Moreover, the data quality assessment mechanism presented in this paper can be used to other application domains by incorporating data quality analysis requirements ontology.

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24

Wilantika, Nori, and Wahyu Catur Wibowo. "Data Quality Management in Educational Data." Jurnal Sistem Informasi 15, no. 2 (October 31, 2019): 52–67. http://dx.doi.org/10.21609/jsi.v15i2.848.

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Every varsity in Indonesia is responsible for ensuring the completeness, the validity, the accuracy, and the currency of its educational data. The educational data is used for implementing higher-education quality assurance system and formulating policies related to universities and majors in Indonesia. Data quality assessment result indicates that educational data in Statistics Polytechnic did not meet completeness, validity, accuracy, and currency criteria. Data quality management maturity has been measured using Loshin’s Data Quality Maturity Model which result is in level 1 to level 2 of maturity. Only the data quality dimensions component has achieved the expected target. Thus, recommendations have been proposed based on the DAMA-DMBOK framework. The activities needed to be carried out are developing and promoting awareness of data quality; defining data quality requirements; profiling, analyzing, and evaluating data quality; define business rules for data quality, establish, and evaluate the data quality services levels, manage problems related to data quality, design and implement operational procedures for data quality management, and monitor operations and performance of data quality management procedures.

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25

Freni, Gabriele, Giorgio Mannina, and Gasparee Viviani. "Assessment of data and parameter uncertainties in integrated water-quality model." Water Science and Technology 63, no. 9 (May 1, 2011): 1913–21. http://dx.doi.org/10.2166/wst.2011.417.

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In integrated urban drainage water quality models, due to the fact that integrated approaches are basically a cascade of sub-models (simulating sewer system, wastewater treatment plant and receiving water body), uncertainty produced in one sub-model propagates to the following ones depending on the model structure, the estimation of parameters and the availability and uncertainty of measurements in the different parts of the system. Uncertainty basically propagates throughout a chain of models in which simulation output from upstream models is transferred to the downstream ones as input. The overall uncertainty can differ from the simple sum of uncertainties generated in each sub-model, depending on well-known uncertainty accumulation problems. The present paper aims to study the uncertainty propagation throughout an integrated urban water-quality model. At this scope, a parsimonious bespoke integrated model has been used allowing analysis of the combinative effect between different sub-models. Particularly, the data and parameter uncertainty have been assessed and compared by means of the variance decomposition concept. The integrated model and the methodology for the uncertainty decomposition have been applied to a complex integrated catchment: the Nocella basin (Italy). The results show that uncertainty contribution due to the model structure is higher with respect to the other sources of uncertainty.

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26

Ginanjar, Mirwan Rofiq, and Sri Mulat Yuningsih. "Penerapan model kendali mutu data hidrologi dalam rangka peningkatan kualitas data." JURNAL SUMBER DAYA AIR 13, no. 2 (February 6, 2018): 131–46. http://dx.doi.org/10.32679/jsda.v13i2.218.

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Planning and management of water resources are dependent on the quality of hydrological data. Hydrological data plays an important role in hydrological analysis. The availability of good and qualified hydrological data is one of the determinants of the results of hydrological analysis. However, the facts indicate that many of the available data do not fit their ideal state. To solve this problem, a hydrological data quality control model should be established in order to improve the quality of national hydrological data. The scope includes quality control of rainfall and discharge data. Analysis of the quality control of rainfall data was conducted on 58 rainfall stations spread on the island of Java. The analysis shows that 41 stations are good categorized, 14 stations are in moderate category and 3 stations are badly categorized. Based on these results, a light improvement scenario was performed, good category Station increased to 46 stations, moderate category decreased to 11 stations and bad category reduced to 1 Stations. Quality control of discharge data analysis was conducted on 14 discharge stations spread on Java Island. Analyzes were performed for QC1, QC2 and QC3 then got final QC value. The results on the final QC show no stations for good category, 2 stations for moderate categories and 12 stations for bad category. Based on the results of the analysis, a light improvement scenario was performed with the result of bad category increased to good category 5 stations, bad category increased to moderate 7 stations, and moderate category 1 stations.

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27

Huang, Yingchun, and Andras Bardossy. "Impacts of Data Quantity and Quality on Model Calibration: Implications for Model Parameterization in Data-Scarce Catchments." Water 12, no. 9 (August 21, 2020): 2352. http://dx.doi.org/10.3390/w12092352.

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The application of hydrological models in data-scarce catchments is usually limited by the amount of available data. It is of great significance to investigate the impacts of data quantity and quality on model calibration—as well as to further improve the understanding of the effective estimation of robust model parameters. How to make adequate utilization of external information to identify model parameters of data-scarce catchments is also worthy of further exploration. HBV (Hydrologiska Byråns Vattenbalansavdelning) models was used to simulate streamflow at 15 catchments using input data of different lengths. The transferability of all calibrated model parameters was evaluated for two validation periods. A simultaneous calibration approach was proposed for data-scarce catchment by using data from the catchment with minimal spatial proximity. The results indicate that the transferability of model parameters increases with the increase of data used for calibration. The sensitivity of data length in calibration varies between the study catchments, while flood events show the key impacts on surface runoff parameters. In general, ten-year data are relatively sufficient to obtain robust parameters. For data-scarce catchments, simultaneous calibration with neighboring catchment may yield more reliable parameters than only using the limited data.

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28

Pedraja-Chaparro, F., J. Salinas-Jimenez, and P. Smith. "On the Quality of the Data Envelopment Analysis Model." Journal of the Operational Research Society 50, no. 6 (June 1999): 636. http://dx.doi.org/10.2307/3010620.

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29

Sattari, Mohammad Taghi, Ali Rezazadeh Joudi, and Andrew Kusiak. "Estimation of Water Quality Parameters With Data-Driven Model." Journal - American Water Works Association 108 (April 1, 2016): E232—E239. http://dx.doi.org/10.5942/jawwa.2016.108.0012.

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30

Hussein Al-Mughni, Nasser Ahmed, Abdullah Saad AL-Malaise AL-Ghamdi, and Farrukh Saleem. "Data Quality Assessment Model for Improving Decision Making Process." IJARCCE 9 (August 30, 2020): 72–77. http://dx.doi.org/10.17148/ijarcce.2020.9814.

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31

Bakar, Anizah Abu, Manmeet Mahinderjit Singh, and Azizul Rahman Mohd Shariff. "Privacy Preservation Quality of Service Model for Data Exposure." Procedia Computer Science 161 (2019): 1139–46. http://dx.doi.org/10.1016/j.procs.2019.11.226.

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32

Pedraja-Chaparro, F., J. Salinas-Jiménez, and P. Smith. "On the quality of the data envelopment analysis model." Journal of the Operational Research Society 50, no. 6 (June 1999): 636–44. http://dx.doi.org/10.1057/palgrave.jors.2600741.

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33

Crumbling, Deana M. "IN SEARCH OF REPRESENTATIVENESS: EVOLVINGTHE ENVIRONMENTAL DATA QUALITY MODEL." Quality Assurance 9, no. 3-4 (July 2002): 179–90. http://dx.doi.org/10.1080/713844024.

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34

Caro, Angelica, Coral Calero, Houari A. Sahraoui, and Mario Piattini. "A Bayesian network to represent a data quality model." International Journal of Information Quality 1, no. 3 (2007): 272. http://dx.doi.org/10.1504/ijiq.2007.016392.

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35

Pan, Jeh-Nan, and Su-Tsu Chen. "Monitoring long-memory air quality data using ARFIMA model." Environmetrics 19, no. 2 (2008): 209–19. http://dx.doi.org/10.1002/env.882.

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36

Aumi, Siam, Brandon Corbett, Tracy Clarke-Pringle, and Prashant Mhaskar. "Data-driven model predictive quality control of batch processes." AIChE Journal 59, no. 8 (March 27, 2013): 2852–61. http://dx.doi.org/10.1002/aic.14063.

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37

Zee, Benny C. "Growth curve model analysis for quality of life data." Statistics in Medicine 17, no. 5-7 (March 15, 1998): 757–66. http://dx.doi.org/10.1002/(sici)1097-0258(19980315/15)17:5/7<757::aid-sim819>3.0.co;2-n.

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38

Xiaohan Ma and Zhigang Deng. "A Statistical Quality Model for Data-Driven Speech Animation." IEEE Transactions on Visualization and Computer Graphics 18, no. 11 (November 2012): 1915–27. http://dx.doi.org/10.1109/tvcg.2012.67.

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39

Cowie, Julie, and Frada Burstein. "Quality of data model for supporting mobile decision making." Decision Support Systems 43, no. 4 (August 2007): 1675–83. http://dx.doi.org/10.1016/j.dss.2006.09.010.

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Iwaniec, Joanna, and Tadeusz Uhl. "Methods for Modal Model Quality Improvement — Measurement Data Pre-processing for Model Identification Process (part I)." Journal of Vibration and Control 13, no. 12 (December 2007): 1679–701. http://dx.doi.org/10.1177/1077546307074574.

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41

Řehák, Jan. "Quality of Data I. Classical Model of Measuring Reliability and its Practical Application." Czech Sociological Review 34, no. 1 (February 1, 1998): 51–60. http://dx.doi.org/10.13060/00380288.1998.34.1.07.

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42

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 (January 28, 2020): 107–26. http://dx.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) creating a platform-independent model (PIM), and (2) converting the created PIM into a platform-specific model (PSM). The PIM comprises informal specifications of data quality. The PSM describes the implementation of a data quality model, thus making it executable, enabling data object scanning and detecting data quality defects and anomalies. The proposed approach was applied to open data sets, analysing their quality. At least 3 advantages were highlighted: (1) a graphical data quality model allows the definition of data quality by non-IT and non-data quality experts as the presented diagrams are easy to read, create and modify, (2) the data quality model allows an analysis of "third-party" data without deeper knowledge on how the data were accrued and processed, (3) the quality of the data can be described at least at two levels of abstraction - informally using natural language or formally by including executable artefacts such as SQL statements.

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Droguett, Enrique López, and Ali Mosleh. "Bayesian Methodology for Model Uncertainty Using Model Performance Data." Risk Analysis 28, no. 5 (October 2008): 1457–76. http://dx.doi.org/10.1111/j.1539-6924.2008.01117.x.

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Setiawan, Atje, and Rudi Rosadi. "SPASIAL DATA MINING MENGGUNAKAN MODEL SAR-KRIGING." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 5, no. 3 (November 19, 2011): 52. http://dx.doi.org/10.22146/ijccs.5213.

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The region of Indonesia is very sparse and it has a variation condition in social, economic and culture, so the problem in education quality at many locations is an interesting topic to be studied. Database used in this research is Base Survey of National Education 2003, while a spatial data is presented by district coordinate as a least analysis unit. The aim of this research is to study and to apply spatial data mining to predict education quality at elementary and junior high schools using SAR-Kriging method which combines an expansion SAR and Kriging method. Spatial data mining process has three stages. preprocessing, process of data mining, and post processing.For processing data and checking model, we built software application of Spatial Data Mining using SAR-Kriging method. An application is used to predict education quality at unsample locations at some cities at DIY Province. The result shows that SAR-Kriging method for some cities at DIY for elementary school has an average percentage error 6.43%. We can conclude that for elementary school, SAR-Kriging method can be used as a fitted model. Keywords— Expansion SAR, SAR-Kriging, quality education

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45

Sumaedi, Sik, Medi Yarmen, and I. Gede Mahatma Yuda Bakti. "Healthcare service quality model." International Journal of Productivity and Performance Management 65, no. 8 (November 14, 2016): 1007–24. http://dx.doi.org/10.1108/ijppm-08-2014-0126.

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Purpose The purpose of this paper is to develop and test a multi-level healthcare service quality (HSQ) model in Jakarta, Indonesia. Design/methodology/approach The research used a quantitative research method. Data were collected via a survey with questionnaire. The respondents are 154 patients of a healthcare institution in Jakarta, Indonesia. Findings The research result shows a multi-level HSQ model. The HSQ model consists of three primary dimensions, namely, healthcare service outcome, healthcare service interaction, and healthcare service environment. Healthcare service outcome has three subdimensions, i.e. waiting time, medicine, and effectiveness. Healthcare service interaction has three dimensions, namely, soft interaction, medical personnel expertise, and hard interaction. Healthcare service environment has two dimensions, which are equipment condition and ambient condition. Research limitations/implications This research was only conducted in one healthcare institution in Jakarta, Indonesia. The data collection using convenience sampling method as well as the use of small sample size caused the limitation of the research results in representing across the customer of the healthcare institution. This study can be replicated with larger sample size and involving more healthcare institutions in order to examine the stability of the HSQ model. Practical implications Healthcare institution’s managers can use the HSQ model to monitor, measure, and improve their service quality. Originality/value There is a lack of research that develops and tests HSQ model based on multi-level approach in the context of developing country. This paper has fulfilled the gap.

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46

Nascimento, Fabiano Rodrigo Alves, Junior Cesar da Rocha, and Ana Cristina Bicharra Garcia. "Automated Evaluation of Open Government Data Portals." International Journal of Electronic Government Research 14, no. 3 (July 2018): 57–72. http://dx.doi.org/10.4018/ijegr.2018070105.

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The quality of public data made available by governments is a crucial factor in achieving greater transparency and also for such data to be used effectively by society. Several models of quality evaluation of open data portals have been proposed in recent years. The vast majority of these models incorporate manual evaluation processes, which makes it time consuming and expensive to maintain a continuous evaluation of open data portals. In order to verify the reliability of the results generated by an automated evaluation model, a comparative analysis was performed between an automated model and a manual model. The results showed the degree of convergence of the quality criteria of the automated model in relation to the manual evaluation model.

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47

Sant'Anna, Ângelo Márcio Oliveira. "Framework of decision in data modeling for quality improvement." TQM Journal 27, no. 1 (January 12, 2015): 135–49. http://dx.doi.org/10.1108/tqm-06-2013-0066.

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Purpose – The purpose of this paper is to propose a framework of decision making to aid practitioners in modeling and optimization experimental data for improvement quality of industrial processes, reinforcing idea that planning and conducting data modeling are as important as formal analysis. Design/methodology/approach – The paper presents an application was carried out about the modeling of experimental data at mining company, with support at Catholic University from partnership projects. The literature seems to be more focussed on the data analysis than on providing a sequence of operational steps or decision support which would lead to the best regression model given for the problem that researcher is confronted with. The authors use the concept of statistical regression technique called generalized linear models. Findings – The authors analyze the relevant case study in mining company, based on best statistical regression models. Starting from this analysis, the results of the industrial case study illustrates the strong relationship of the improvement process with the presented framework approach into practice. Moreover, the case study consolidating a fundamental advantage of regression models: modeling guided provides more knowledge about products, processes and technologies, even in unsuccessful case studies. Research limitations/implications – The study advances in regression model for data modeling are applicable in several types of industrial processes and phenomena random. It is possible to find unsuccessful data modeling due to lack of knowledge of statistical technique. Originality/value – An essential point is that the study is based on the feedback from practitioners and industrial managers, which makes the analyses and conclusions from practical points of view, without relevant theoretical knowledge of relationship among the process variables. Regression model has its own characteristics related to response variable and factors, and misspecification of the regression model or their components can yield inappropriate inferences and erroneous experimental results.

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48

Zhao, Qun, Yuelong Zhu, Dingsheng Wan, Yufeng Yu, and Xifeng Cheng. "Research on the Data-Driven Quality Control Method of Hydrological Time Series Data." Water 10, no. 12 (November 23, 2018): 1712. http://dx.doi.org/10.3390/w10121712.

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Ensuring the quality of hydrological data has become a key issue in the field of hydrology. Based on the characteristics of hydrological data, this paper proposes a data-driven quality control method for hydrological data. For continuous hydrological time series data, two combined forecasting models and one statistical control model are constructed from horizontal, vertical, and statistical perspectives and the three models provide three confidence intervals. Set the suspicious level based on the number of confidence intervals for data violations, control the data, and provide suggested values for suspicious and missing data. For the discrete hydrological data with large time-space difference, the similar weight topological map between the neighboring stations is established centering on the hydrological station under the test and it is adjusted continuously with the seasonal changes. Lastly, a spatial interpolation model is established to detect the data. The experimental results show that the quality control method proposed in this paper can effectively detect and control the data, find suspicious and erroneous data, and provide suggested values.

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Marsili-Libelli, S., E. Caporali, S. Arrighi, and C. Becattelli. "A georeferenced river quality model." Water Science and Technology 43, no. 7 (April 1, 2001): 223–30. http://dx.doi.org/10.2166/wst.2001.0429.

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Water quality models have reached a high degree of sophistication, but their weak side remains user interface and output georeferencing. The aim of this paper is to propose an interfacing procedure between two widespread but specialised programming environments: ArcVIEW as a Geographical Information System (GIS) and Matlab as a scientific programming tool for numerical analysis. The proposed solution is based on a Dynamic Data Exchange (DDE) between the two programs in order to operate a Matlab-based water quality model from within the GIS environment. It is shown how special GIS objects must be created and how they operate to achieve the goal of having quality data created by the model placed on a geographical map, together with other site features.

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Kubíčková, Dana, and Vladimír Nulíček. "Bankruptcy Model Construction and its Limitation in Input Data Quality." Journal of Business and Economics 10, no. 2 (February 20, 2019): 117–25. http://dx.doi.org/10.15341/jbe(2155-7950)/02.10.2019/003.

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The aim of the research project solved at the University of Finance and administration is to construct a new bankruptcy model. The intention is to use data of the firms that have to cease their activities due to bankruptcy. The most common method for bankruptcy model construction is multivariate discriminant analyses (MDA). It allows to derive the indicators most sensitive to the future companies’ failure as a parts of the bankruptcy model. One of the assumptions for using the MDA method and reassuring the reliable results is the normal distribution and independence of the input data. The results of verification of this assumption as the third stage of the project are presented in this article. We have revealed that this assumption is met only in a few selected indicators. Better results were achieved in the indicators in the set of prosperous companies and one year prior the failure. The selected indicators intended for the bankruptcy model construction thus cannot be considered as suitable for using the MDA method.

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