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Journal articles on the topic 'TESTING DATA'

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Author: Grafiati

Published: 11 September 2023

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1

Kumar, Gagan, and Vinay Chopra. "Automatic Test Data Generation for Basis Path Testing." Indian Journal Of Science And Technology 15, no. 41 (November 5, 2022): 2151–61. http://dx.doi.org/10.17485/ijst/v15i41.1503.

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2

Golfarelli, Matteo, and Stefano Rizzi. "Data Warehouse Testing." International Journal of Data Warehousing and Mining 7, no. 2 (April 2011): 26–43. http://dx.doi.org/10.4018/jdwm.2011040102.

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Testing is an essential part of the design life-cycle of a software product. Although most phases of data warehouse design have received considerable attention in the literature, not much research has been conducted concerning data warehouse testing. In this paper, the authors introduce a number of data mart-specific testing activities, classify them in terms of what is tested and how it is tested, and show how they can be framed within a reference design method to devise a comprehensive and scalable approach. Finally, the authors discuss some practical evidences emerging from a real case study.

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3

Uzych, Leo. "Genetic Testing Data." Journal of Occupational & Environmental Medicine 38, no. 1 (January 1996): 13–14. http://dx.doi.org/10.1097/00043764-199601000-00001.

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4

Rischitelli, Gary. "Genetic Testing Data." Journal of Occupational & Environmental Medicine 38, no. 1 (January 1996): 14. http://dx.doi.org/10.1097/00043764-199601000-00002.

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5

Walczak, Przemysław, and Jadwiga Daszyńska-Daszkiewicz. "Testing microphysics data." Proceedings of the International Astronomical Union 9, S301 (August 2013): 221–28. http://dx.doi.org/10.1017/s1743921313014361.

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AbstractHigh precision asteroseismic data provide a unique opportunity to test input microphysics such as stellar opacities, chemical composition or equation of state. These tests are possible because pulsational frequencies as well as amplitudes and phases of the light variations are very sensitive to the internal structure of a star. We can therefore compute pulsation models and compare them with observations. The agreement or differences should tell us whether some models are adequate or not, and which input data need to be improved.

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6

Surname, Bob. "Testing CrossMark data." Journal of CrossMark Testing 1, no. 2 (2011): 20. http://dx.doi.org/10.5555/cm_test_2.

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7

Harrold, Mary Jean, and Mary Lou Soffa. "Interprocedual data flow testing." ACM SIGSOFT Software Engineering Notes 14, no. 8 (December 1989): 158–67. http://dx.doi.org/10.1145/75309.75327.

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8

Krueger, Alan B. "Stress Testing Economic Data." Business Economics 45, no. 2 (April 2010): 110–15. http://dx.doi.org/10.1057/be.2010.4.

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9

Faitelson, David, and Shmuel Tyszberowicz. "Data refinement based testing." International Journal of System Assurance Engineering and Management 2, no. 2 (June 2011): 144–54. http://dx.doi.org/10.1007/s13198-011-0060-y.

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10

Ray, L. Bryan. "Testing biochemical data by simulation." Science 369, no. 6502 (July 23, 2020): 387.10–389. http://dx.doi.org/10.1126/science.369.6502.387-j.

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11

Tarry, Claudia. "Misleading data from animal testing." Nursing Standard 18, no. 18 (January 14, 2004): 31. http://dx.doi.org/10.7748/ns.18.18.31.s47.

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12

Kashyap, Shubhankar, and Avantika Singh. "Testing Data Feminism in India." Scholars Journal of Arts, Humanities and Social Sciences 9, no. 10 (May 25, 2021): 516–30. http://dx.doi.org/10.36347/sjahss.2021.v09i10.006.

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Catherine D’Ignazio and Lauren F.Klein in their book Data Feminism argue that data models reflect existing power structures and social hierarchies. We aim to test this hypothesis in India on Instagram. Instagram is one of the most accessible and politically engaging social media platforms in India which makes its data models appropriate subjects for our study. The research question of our study is “Do Instagram data models disproportionately prioritise accounts that publish majoritarian feminist content over intersectional feminist content in India?” The paper employs two methodological approaches; an experimental set-up under controlled setting for primary data collection through a positivist sociological approach kept under time bound observational study and secondary data qualitative analysis. This paper first analyses the biases and preconceived notions which cloud digital data models. It further elaborates upon the concept of Data Justice which acknowledges historical inequalities and power differentials amongst communities that drive data collection. The paper attempts to test this hypothesis through an experiment. The experiment includes creation of two Instagram accounts dedicated to two different forms of feminism. Account “A” would publish content ascribing to popular feminist ideals i.e. non-intersectional and Account “B” would publish intersectional feminist content. The creation of new accounts is critical for establishing a causal relationship between data models and disparity in accounts growth, as a pre-established follower count would affect the accounts engagement. For a period of three months, both the accounts will employ the same strategies to increase user engagement/reach. The impact of these strategies on metrics such as follower count, post likes, post reshares, post comments, profile visits, frequency and duration of story views, and duration of post visibility would be documented through a weekly monitoring system. The data collected......

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13

Filippov, B. I., and T. B. Trush. "Processing of dependability testing data." Dependability 17, no. 2 (January 1, 2017): 24–30. http://dx.doi.org/10.21683/1729-2646-2017-17-2-24-30.

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14

G, Zolzaya, and Usukhbayar B. "Hypothesis testing of agricultural data." Mongolian Journal of Agricultural Sciences 25, no. 03 (December 28, 2018): 132–37. http://dx.doi.org/10.5564/mjas.v25i03.1182.

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The sustainable development strategy of the crop to implement environmental sustainability to be economically efficient, directed to improve soil productivity of the field and to increase the amount of harvest and to fully supply needs of food production by using research analysis. There are many types of methods for hypothesis testing in mathematical and statistical analyses. From these methods, T-stat and F-stat are widely used. However, they are in sufficient to express the final results, therefore we suggest to use Wald test, Variables test and Reset test. In addition to the quality of soil, wheat harvest also affects many factors, including climate, landscape, technology, and ecology. May-August precipitation (mm) and average air temperature (0C), the number of sunspot activation and the amount of crop in the Central region 1970-2015 are selected and converted to standardized average values, and done correlation coefficients analysis and multiple-factor regression analysis, hypotheses and tests. The assumptions should be regularly reviewed and verified to eliminate uncertainties in the reliability of quantitative data in agricultural research and analysis. The following conclusions have been made to examine Wald test, Variables test, and Reset test hypotheses based on the Central and Regional Weather data. The main factors that increase wheat harvest are the amount of precipitation. The precipitation is recognized for all assumptions. Other indicators (wind speed, landscape etc.) need to be selected rather than average air temperature in harvest. Хөдөө аж ахуйн тоон мэдээлэлд таамаглал дэвшүүлж, шалгах Хураангуй: Ургамал өсч хөгжих, ургац бүрэлдэх үйл явц нь гадаад орчны олон хүчин зүйлийн шууд нөлөөгөөр ургамалд явагдах тодорхой биологи, физиологи, биофизик, зарим талаар биохимийн хуулиудад ямар нэгэн байдлаар захирагддаг. Ургамал ургах, ургац бүрэлдэх явцыг дээрх хуулиудын хүрээнд авч үзэхдээ ургамал болон түүний хүрээлэн буй орчны хоорондох зүй тогтлыг математик, статистикийн аргаар илэрхийлж, ургамалын ургах бүхий л үйл явцыг загварчлах боломжтой. ХАА-н судалгаанд түүврийн тоо мэдээнд тулгуурласан параметр нь эх олонлогийг төлөөлж чадах эсэх талаар таамаглал дэвшүүлж, шалгах шаардлага зайлшгүй тулгардаг. Математик, статистикийн шинжилгээнд таамаглал дэвшүүлж, шалгах олон төрлийн арга байдгаас Стьюдентийн шинжүүрээр, Фишерийн шинжүүрийг өргөн ашигладаг ба эдгээр нь эцсийн үр дүнг илэрхийлэхэд хангалтгүй, тиймээс Wald test, Variables test, Reset test ашиглах шаардлагатай. Түлхүүр үг: Хамаарлын шинжилгээ, загварын ач холбогдол, хувьсагчдын шалгуур

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15

Alizadeh Noughabi, Hadi, and Naser Reza Arghami. "Testing exponentiality using transformed data." Journal of Statistical Computation and Simulation 81, no. 4 (April 2011): 511–16. http://dx.doi.org/10.1080/00949650903348171.

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16

Gupta, Sunil. "Clinical data acceptance testing procedure." Pharmaceutical Programming 1, no. 2 (December 2008): 97–107. http://dx.doi.org/10.1179/175709206x372272.

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17

Rubin, Allen. "Significance Testing with Population Data." Social Service Review 59, no. 3 (September 1985): 518–20. http://dx.doi.org/10.1086/644316.

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18

Jorgensen, Alan A. "Testing with hostile data streams." ACM SIGSOFT Software Engineering Notes 28, no. 2 (March 2003): 9. http://dx.doi.org/10.1145/638750.638781.

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19

Hesse-Biber, Sharlene, and Paul Dupuis. "Testing Hypotheses on Qualitative Data." Social Science Computer Review 18, no. 3 (August 2000): 320–28. http://dx.doi.org/10.1177/089443930001800307.

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20

Alizadeh Noughabi, Hadi, and Naser Reza Arghami. "Testing Normality Using Transformed Data." Communications in Statistics - Theory and Methods 42, no. 17 (September 2, 2013): 3065–75. http://dx.doi.org/10.1080/03610926.2011.611604.

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21

Konoplev, A. V., A. A. Bulgakov, V. E. Popov, O. F. Popov, A. V. Scherbak, Yu V. Shveikin, and F. O. Hoffman. "Model Testing Using Chernobyl Data." Health Physics 70, no. 1 (January 1996): 8–12. http://dx.doi.org/10.1097/00004032-199601000-00002.

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22

Kryshev, I. I., T. G. Sazykina, I. N. Ryabov, V. K. Chumak, and O. L. Zarubin. "Model Testing Using Chernobyl Data." Health Physics 70, no. 1 (January 1996): 13–17. http://dx.doi.org/10.1097/00004032-199601000-00003.

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23

Garger, E. K., F. O. Hoffman, and C. W. Miller. "Model Testing Using Chernobyl Data." Health Physics 70, no. 1 (January 1996): 18–24. http://dx.doi.org/10.1097/00004032-199601000-00004.

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24

Linnet, Kristian. "Testing Normality of Transformed Data." Applied Statistics 37, no. 2 (1988): 180. http://dx.doi.org/10.2307/2347337.

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25

Mansour, Nashat, and Miran Salame. "Data Generation for Path Testing." Software Quality Journal 12, no. 2 (June 2004): 121–36. http://dx.doi.org/10.1023/b:sqjo.0000024059.72478.4e.

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26

Varrella, Gary F. "Using Water-Testing Data Sets." Science Activities: Classroom Projects and Curriculum Ideas 31, no. 3 (September 1994): 9–14. http://dx.doi.org/10.1080/00368121.1994.10113142.

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27

Curto, A., J. Aumont, J. F. Macías-Pérez, E. Martínez-González, R. B. Barreiro, D. Santos, F. X. Désert, and M. Tristram. "Testing Gaussianity on Archeops data." Astronomy & Astrophysics 474, no. 1 (August 13, 2007): 23–33. http://dx.doi.org/10.1051/0004-6361:20066881.

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28

Pfeifer, Johannes, Michael Wahl, and Hans Wojtkowiak. "Data exchange formats for testing." Microprocessing and Microprogramming 27, no. 1-5 (August 1989): 687–93. http://dx.doi.org/10.1016/0165-6074(89)90133-6.

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29

Kafala, S. I., T. D. MacMahon, and P. W. Gray. "Testing of data evaluation methods." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 339, no. 1-2 (January 1994): 151–57. http://dx.doi.org/10.1016/0168-9002(94)91796-5.

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30

Laski, Janusz. "Data flow testing in STAD." Journal of Systems and Software 12, no. 1 (April 1990): 3–14. http://dx.doi.org/10.1016/0164-1212(90)90062-q.

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31

Woronow, Alex, Karen M. Love, and John C. Butler. "Interpreting and testing compositional data." Mathematical Geology 21, no. 1 (January 1989): 61–63. http://dx.doi.org/10.1007/bf00897240.

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32

Bashkansky, Emil, and Vladimir Turetsky. "Proficiency testing: binary data analysis." Accreditation and Quality Assurance 21, no. 4 (April 21, 2016): 265–70. http://dx.doi.org/10.1007/s00769-016-1208-x.

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33

Knottnerus, J. André, and Peter Tugwell. "Multiple testing in big data." Journal of Clinical Epidemiology 67, no. 8 (August 2014): 843–44. http://dx.doi.org/10.1016/j.jclinepi.2014.05.009.

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34

Zhang, Beige, Chun Li, Nazaraf Shah, Xiang Fei, Lihong Jiang, and Hongming Cai. "A testing data validity assessment method and testing data validation platform based on SOA." Service Oriented Computing and Applications 12, no. 3-4 (September 27, 2018): 201–9. http://dx.doi.org/10.1007/s11761-018-0242-4.

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35

Castro, Laura M., and Thomas Arts. "Testing Data Consistency of Data-Intensive Applications Using QuickCheck." Electronic Notes in Theoretical Computer Science 271 (March 2011): 41–62. http://dx.doi.org/10.1016/j.entcs.2011.02.010.

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36

McIntyre, Lauren M., and B. S. Weir. "Hardy-Weinberg Testing for Continuous Data." Genetics 147, no. 4 (December 1, 1997): 1965–75. http://dx.doi.org/10.1093/genetics/147.4.1965.

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Abstract Estimation of allelic and genotypic distributions for continuous data using kernel density estimation is discussed and illustrated for some variable number of tandem repeat data. These kernel density estimates provide a useful representation of data when only some of the many variants at a locus are present in a sample. Two Hardy-Weinberg test procedures are introduced for continuous data: a continuous chi-square test with test statistic TCCS and a test based on Hellinger's distance with test statistic TCCS. Simulations are used to compare the powers of these tests to each other and to the powers of a test of intraclass correlation TIC, as well as to the power of Fisher's exact test TFET applied to discretized data. Results indicate that the power of TCCS is better than that of THD but neither is as powerful as TFET. The intraclass correlation test does not perform as well as the other tests examined in this article.

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37

Sun, Yanqing, and Jimin Lee. "Testing independent censoring for longitudinal data." Statistica Sinica 21, no. 3 (June 1, 2011): 1315–39. http://dx.doi.org/10.5705/ss.2009.251.

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38

ORIMO, ATSUSHI. "Graphical output of health testing data." Japanese journal of MHTS 12, no. 4 (1985): 323–30. http://dx.doi.org/10.7143/jhep1985.12.323.

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39

Orimo, Atsushi, and Masao Wakabayashi. "Graphical output of health testing data." Medical Informatics 15, no. 2 (January 1990): 141–49. http://dx.doi.org/10.3109/14639239008997666.

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40

Gaulier, Hurlin, and Jean-Pierre. "Testing Convergence: A Panel Data Approach." Annales d'Économie et de Statistique, no. 55/56 (1999): 411. http://dx.doi.org/10.2307/20076205.

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41

Andreev, A. V. "Probability, statistical optics, and data testing." Uspekhi Fizicheskih Nauk 162, no. 1 (1992): 147. http://dx.doi.org/10.3367/ufnr.0162.199201g.0147.

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42

Svalov, A. M. "Data Processing Method of Well Testing." Oil and Gas Technologies 134, no. 3 (2021): 35–38. http://dx.doi.org/10.32935/1815-2600-221-134-3-35-38.

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Horner’s traditional method of processing well test data can be improved by a special transformation of the pressure curves, which reduces the time the converted curves reach the asymptotic regimes necessary for processing these data. In this case, to take into account the action of the «skin factor» and the effect of the wellbore, it is necessary to use a more complete asymptotic expansion of the exact solution of the conductivity equation at large values of time. At the same time, this method does not allow to completely eliminate the influence of the wellbore, since the used asymptotic expansion of the solution for small values of time is limited by the existence of a singular point, in the vicinity of which the asymptotic expansion ceases to be valid. To solve this problem, a new method of processing well test data is proposed, which allows completely eliminating the influence of the wellbore. The method is based on the introduction of a modified inflow function to the well, which includes a component of the boundary condition corresponding to the influence of the wellbore.

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43

Dmitruk, V. V., A. A. Kasyanenko, E. B. Kovinchenko, and I. V. Kravchenko. "Data Processing Method of Well Testing." Oil and Gas Technologies 134, no. 3 (2021): 55–59. http://dx.doi.org/10.32935/1815-2600-221-134-3-55-59.

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This publication discusses research work to improve the existing process for cooling compressed gas at the Yuzhno-Russkoye oil and gas condensate field of OJSC «Severneftegazprom». This project consisted in a scientific study of technological processes for the treatment, compression and transportation of natural gas. Based on the results of the research project, a system for additional cooling of compressed gas after the gas pumping units of the booster compressor station was developed (independent of the ambient temperature). When implementing into the existing technological process of cooling the compressed gas, the developed system will provide the required temperature parameters of the natural gas together with the operated air cooling units of gas and recuperative heat exchangers. To select an additional gas cooling system providing the required cooling capacity, taking into account existing energy resources (electricity, natural gas) and climatic conditions of the Yuzhno-Russkoe oil and gas condensate field, a detailed modeling of the different options of cooling systems of gas actually available on the market of this equipment in Russia and the option selection with the highest rates of integral effect and performance index. The proposed solution guarantees a stable supply of commercial natural gas within the framework of contractual obligations and excludes the influence of temperature factors of the environment on technological processes.

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44

Bakraev, М. М., F. Z. Bulyukova, E. B. Dumler, T. B. Soltukiev, and I. E. Ordashev. "Data Processing Method of Well Testing." Oil and Gas Technologies 134, no. 3 (2021): 39–42. http://dx.doi.org/10.32935/1815-2600-221-134-3-39-42.

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The article presents an analysis of technological schemes for the development of the XXIII layer of the Goyt-Kort field, as a result of which the implementation of measures for high pressure gas injection is proposed. The geological and production characteristics of the reservoir, technological features of the developed process and economic needs justified the choice of the Goyt-Кort field as a priority object for high-pressure gas injection to enhance oil recovery. As a result of the proposed measures, an increase in reservoir pressure was noted in the area of the first row of production wells, an improvement in their performance due to gas injection.The characteristic reasons for the frequent well workovers caused by deposits of asphalt-resin-paraffin substances in equipment, in tubing and flow lines have been identified. Measures are provided to eliminate and combat the listed complications. Thus, the field tested and implemented the technology of constant dosing of the inhibitor into the annulus of the well. Comparative testing of the activity of the reagents was carried out. To remove the clay bridging agent of the pore matrix of high-temperature terrigenous objects, the use of reagents based on boronfluorinehydrogen acid is recommended.

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45

Gupta, Dr S. L., Dr Payal Pahwa, and Ms Sonali Mathur. "CLASSIFICATION OF DATA WAREHOUSE TESTING APPROACHES." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 3 (December 2, 2012): 381–86. http://dx.doi.org/10.24297/ijct.v3i3a.2942.

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Data Warehouse is a collection of large amount of data which is used by the management for making strategic decisions. The data in a data warehouse is gathered from heterogeneous sources and then populated and queried for carrying out the analysis. The data warehouse design must support the queries for which it is being used for. The design is often an iterative process and must be modified a number of times before any model can be stabilized. The design life cycle of any product includes various stages wherein, testing being the most important one. Data warehouse design has received considerable attention whereas data warehouse testing is being explored now by various researchers. This paper discusses about various categories of testing activities being carried out in a data warehouse at different levels

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46

Weller, Edie A., and Louise M. Ryan. "Testing for Trend with Count Data." Biometrics 54, no. 2 (June 1998): 762. http://dx.doi.org/10.2307/3109782.

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47

Te Han. "Hypothesis testing with multiterminal data compression." IEEE Transactions on Information Theory 33, no. 6 (November 1987): 759–72. http://dx.doi.org/10.1109/tit.1987.1057383.

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48

Meintanis, Simos G. "Testing for normality with panel data." Journal of Statistical Computation and Simulation 81, no. 11 (November 2011): 1745–52. http://dx.doi.org/10.1080/00949655.2010.503654.

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49

Su, Ting, Ke Wu, Weikai Miao, Geguang Pu, Jifeng He, Yuting Chen, and Zhendong Su. "A Survey on Data-Flow Testing." ACM Computing Surveys 50, no. 1 (April 13, 2017): 1–35. http://dx.doi.org/10.1145/3020266.

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50

Robbins, Michael, Colin Gallagher, Robert Lund, and Alexander Aue. "Mean shift testing in correlated data." Journal of Time Series Analysis 32, no. 5 (January 24, 2011): 498–511. http://dx.doi.org/10.1111/j.1467-9892.2010.00707.x.

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