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Journal articles on the topic 'Data requirements'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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1

Iezzoni, Lisa I. "Data Requirements." Medical Care 28, no. 9 (September 1990): 758–59. http://dx.doi.org/10.1097/00005650-199009000-00003.

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2

Aiken, Peter, Youngohc Yoon, and Belkis Leong-Hong. "Requirements-driven data engineering." Information & Management 35, no. 3 (March 1999): 155–68. http://dx.doi.org/10.1016/s0378-7206(98)00082-2.

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3

Taylor‐Adams, Sally, and Barry Kirwan. "Human reliability data requirements." International Journal of Quality & Reliability Management 12, no. 1 (February 1995): 24–46. http://dx.doi.org/10.1108/02656719510076221.

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4

Jukic, Nenad, and Miguel Velasco. "Data Warehousing Requirements Collection and Definition." International Journal of Business Intelligence Research 1, no. 3 (July 2010): 66–76. http://dx.doi.org/10.4018/jbir.2010070105.

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Defining data warehouse requirements is widely recognized as one of the most important steps in the larger data warehouse system development process. This paper examines the potential risks and pitfalls within the data warehouse requirement collection and definition process. A real scenario of a large-scale data warehouse implementation is given, and details of this project, which ultimately failed due to inadequate requirement collection and definition process, are described. The presented case underscores and illustrates the impact of the requirement collection and definition process on the data warehouse implementation, while the case is analyzed within the context of the existing approaches, methodologies, and best practices for prevention and avoidance of typical data warehouse requirement errors and oversights.
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5

Okcu, Hüseyin. "Operational Requirements of Unmanned Aircraft Systems Data Link and Communication Systems." Journal of Advances in Computer Networks 4, no. 1 (2016): 28–32. http://dx.doi.org/10.18178/jacn.2016.4.1.199.

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6

RAYNER, R. F. "TECHNICAL NOTE. FIELD DATA REQUIREMENTS." Proceedings of the Institution of Civil Engineers - Water Maritime and Energy 96, no. 3 (September 1992): 201–3. http://dx.doi.org/10.1680/iwtme.1992.21091.

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7

Alekseev, A. P., M. I. Makarov, and V. V. Orlov. "Data protection educational material requirements." Infokommunikacionnye tehnologii 13, no. 3 (September 2015): 333–38. http://dx.doi.org/10.18469/ikt.2015.13.3.15.

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8

Barbará, Daniel. "Requirements for clustering data streams." ACM SIGKDD Explorations Newsletter 3, no. 2 (January 2002): 23–27. http://dx.doi.org/10.1145/507515.507519.

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9

Beaton, George H. "Nutrient requirements and population data." Proceedings of the Nutrition Society 47, no. 1 (February 1988): 63–78. http://dx.doi.org/10.1079/pns19880011.

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10

Maalej, Walid, Maleknaz Nayebi, Timo Johann, and Guenther Ruhe. "Toward Data-Driven Requirements Engineering." IEEE Software 33, no. 1 (January 2016): 48–54. http://dx.doi.org/10.1109/ms.2015.153.

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11

Innes, Judith. "The Power of Data Requirements." Journal of the American Planning Association 54, no. 3 (September 30, 1988): 275–78. http://dx.doi.org/10.1080/01944368808976488.

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12

Ghattas, Johny, Mor Peleg, Pnina Soffer, and Yaron Denekamp. "Data Requirements for Process Learning." International Journal of Knowledge-Based Organizations 3, no. 1 (January 2013): 1–18. http://dx.doi.org/10.4018/ijkbo.2013010101.

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Process flexibility and adaptability is essential in environments where the processes are prompt to changes and variations. Process learning is a possible approach for automatically discovering from process log data those process paths that yielded good outcomes and suggesting appropriate process model modifications to enhance future process performance in such environments. The authors discuss and establish the data requirements for process learning, applicable to clinical process management. Their discussion extends a previously established learning process model (LPM) by providing a formal set of data requirements which enables the authors to accomplish effective learning. Learning data requirements are illustrated by walking through the application of the LPM framework to a clinical process.
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13

Heinrich, Bernd, Diana Hristova, Mathias Klier, Alexander Schiller, and Michael Szubartowicz. "Requirements for Data Quality Metrics." Journal of Data and Information Quality 9, no. 2 (January 22, 2018): 1–32. http://dx.doi.org/10.1145/3148238.

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14

Forrest, R. A. "Data requirements for neutron activation." Fusion Engineering and Design 81, no. 18 (August 2006): 2143–56. http://dx.doi.org/10.1016/j.fusengdes.2006.01.001.

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15

Forrest, R. A. "Data requirements for neutron activation." Fusion Engineering and Design 81, no. 18 (August 2006): 2157–74. http://dx.doi.org/10.1016/j.fusengdes.2006.01.002.

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16

Abrams, Jeffrey, Robert Erwin, Gwen Fyfe, and Richard L. Schilsky. "Data Submission Standards and Evidence Requirements." Oncologist 15, no. 5 (April 9, 2010): 488–91. http://dx.doi.org/10.1634/theoncologist.2009-0260.

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17

Shaposhnikov, Y. A. "DATA QUALITY TRAINING MEET INTERNATIONAL REQUIREMENTS." International Journal of Applied and Fundamental Research (Международный журнал прикладных и фундаментальных исследований) 2, no. 5 2017 (2017): 352–55. http://dx.doi.org/10.17513/mjpfi.11603.

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18

Garcia, Jorge Esparteiro, and Ana C. R. Paiva. "Maintaining Requirements Using Web Usage Data." Procedia Computer Science 100 (2016): 626–33. http://dx.doi.org/10.1016/j.procs.2016.09.204.

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19

Baker, E. N., T. L. Blundell, M. Vijayan, E. Dodson, G. Dodson, G. L. Gilliland, and J. L. Sussman. "Archival journal requirements for data deposition." Biophysical Journal 70, no. 6 (June 1996): 2994. http://dx.doi.org/10.1016/s0006-3495(96)79869-7.

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20

Alecian, G. "Radiative accelerations and atomic data requirements." EAS Publications Series 17 (2005): 33–42. http://dx.doi.org/10.1051/eas:2005096.

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21

Moura e Sá, Patrícia, and Rita Martins. "Data quality requirements for water bills." TQM Journal 28, no. 6 (October 10, 2016): 933–53. http://dx.doi.org/10.1108/tqm-12-2014-0108.

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Purpose The purpose of this paper is to uncover the customers’ concerns with the information disclosed in water services invoices and to analyse them with reference to the data quality dimensions usually proposed in the literature. In the context of services of general interest invoices are particularly relevant as a vehicle to convey information to all consumers. Design/methodology/approach Based on the principles of quality planning, the research uses a qualitative approach to identify the data quality requirements of water invoices. Customer voices were collected by means of focus groups and their meanings analysed using an affinity diagram. Findings Findings show that plain language efforts and strategies to enhance trust on the service provided need to be further reinforced. Consumers’ requirements together with the regulator recommendations also confirm the data quality dimensions identified in the literature. Practical implications This research highlights that avoiding technical language and making visible the consequences of different consumption levels on the amounts to be paid is essential when designing water invoices. Moreover, it emphasises that there is still room for improvement in the way the economic regulator performs its role in ensuring the provision of sound information. Originality/value This research addresses a literature gap by conducting a study on data quality requirements outside the context of information systems for organisations. The study is original because it looks at water invoices as a “product” that can be designed to meet the needs of their users.
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22

JUST, W. "Data Requirements of Reverse-Engineering Algorithms." Annals of the New York Academy of Sciences 1115, no. 1 (October 9, 2007): 142–53. http://dx.doi.org/10.1196/annals.1407.008.

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23

Immonen, Anne, and Jarmo Kalaoja. "Requirements of an Energy Data Ecosystem." IEEE Access 7 (2019): 111692–708. http://dx.doi.org/10.1109/access.2019.2933919.

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24

Wang, Sophia Y., Suzann Pershing, and Aaron Y. Lee. "Big data requirements for artificial intelligence." Current Opinion in Ophthalmology 31, no. 5 (July 9, 2020): 318–23. http://dx.doi.org/10.1097/icu.0000000000000676.

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25

Biddle, John C. "Instructional data requirements from administrative systems." ACM SIGUCCS Newsletter 22, no. 2 (June 15, 1992): 10–15. http://dx.doi.org/10.1145/142086.142092.

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26

Giesecke, J., and U. Haberlandt. "Precipitation Data Requirements for Urban Hydrology." Water International 23, no. 2 (June 1998): 60–66. http://dx.doi.org/10.1080/02508069808686743.

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27

Perko, Igor, and Peter Ototsky. "Business ecosystems requirements for big data." International Journal of Transitions and Innovation Systems 5, no. 3/4 (2016): 329. http://dx.doi.org/10.1504/ijtis.2016.083589.

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28

Perko, Igor, and Peter Ototsky. "Business ecosystems requirements for big data." International Journal of Transitions and Innovation Systems 5, no. 3/4 (2016): 329. http://dx.doi.org/10.1504/ijtis.2016.10004471.

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29

Sun, X., H. Wang, J. Li, and Y. Zhang. "Satisfying Privacy Requirements Before Data Anonymization." Computer Journal 55, no. 4 (March 17, 2011): 422–37. http://dx.doi.org/10.1093/comjnl/bxr028.

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30

Shenvi, Edna C., Daniella Meeker, and Aziz A. Boxwala. "Understanding data requirements of retrospective studies." International Journal of Medical Informatics 84, no. 1 (January 2015): 76–84. http://dx.doi.org/10.1016/j.ijmedinf.2014.10.004.

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31

Innes, Judith. "Effects of data requirements on planning." Computers, Environment and Urban Systems 12, no. 2 (January 1988): 77–88. http://dx.doi.org/10.1016/0198-9715(88)90011-7.

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32

Shirey, Robert W. "Security requirements for network management data." Computer Standards & Interfaces 17, no. 4 (September 1995): 321–31. http://dx.doi.org/10.1016/0920-5489(95)00012-j.

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33

Jarnevich, Catherine S., James J. Graham, Gregory J. Newman, Alycia W. Crall, and Thomas J. Stohlgren. "Balancing data sharing requirements for analyses with data sensitivity." Biological Invasions 9, no. 5 (November 14, 2006): 597–99. http://dx.doi.org/10.1007/s10530-006-9042-4.

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34

Kovacs, Adam Tamas, and Andras Micsik. "BIM quality control based on requirement linked data." International Journal of Architectural Computing 19, no. 3 (May 13, 2021): 431–48. http://dx.doi.org/10.1177/14780771211012175.

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This article discusses a BIM Quality Control Ecosystem that is based on Requirement Linked Data in order to create a framework where automated BIM compliance checking methods can be widely used. The meaning of requirements is analyzed in a building project context as a basis for data flow analysis: what are the main types of requirements, how they are handled, and what sources they originate from. A literature review has been conducted to find the present development directions in quality checking, besides a market research on present, already widely used solutions. With the conclusions of these research and modern data management theory, the principles of a holistic approach have been defined for quality checking in the Architecture, Engineering and Construction (AEC) industry. A comparative analysis has been made on current BIM compliance checking solutions according to our review principles. Based on current practice and ongoing research, a state-of-the-art BIM quality control ecosystem is proposed that is open, enables automation, promotes interoperability, and leaves the data governing responsibility at the sources of the requirements. In order to facilitate the flow of requirement and quality data, we propose a model for requirements as Linked Data and provide example for quality checking using Shapes Constraint Language (SHACL). As a result, an opportunity is given for better quality and cheaper BIM design methods to be implemented in the industry.
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35

Davidson, Bruce, Yang W. Lee, and Richard Wang. "Developing data production maps: meeting patient discharge data submission requirements." International Journal of Healthcare Technology and Management 6, no. 2 (2004): 223. http://dx.doi.org/10.1504/ijhtm.2004.004978.

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36

Williams, P. H., C. R. Margules, and D. W. Hilbert. "Data requirements and data sources for biodiversity priority area selection." Journal of Biosciences 27, no. 4 (July 2002): 327–38. http://dx.doi.org/10.1007/bf02704963.

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37

Rodricks, Joseph V., and Stephen L. Brown. "Data Requirements for Assessment of Device Risks." Journal of the American College of Toxicology 7, no. 4 (July 1988): 509–18. http://dx.doi.org/10.3109/10915818809019525.

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The tools of risk assessment are now applied in the regulation of most classes of commercially produced chemicals, and there appear to be good reasons to apply the technique to potentially hazardous chemical constituents of medical devices. If it is to be applied, several sets of issues and data requirements almost unique to device constituents risk assessment need to be explored. Most important of these are the issues of inter-route extrapolation and assessment of human exposure to device constituents. An example of the latter problem, involving residues of the device sterilant ethylene oxide, is provided to reveal the types of data needed to assess human exposure to device constituents. It also reveals the type of device usage database that has been developed to assist exposure assessment. Several suggestions for dealing with data gaps in all types of device risk assessment are provided.
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38

Rectanus, M., and D. Peters. "Global Data Sharing: Requirements from Chemical Regulation." International Chemical Regulatory and Law Review 2, no. 2 (2019): 61–64. http://dx.doi.org/10.21552/icrl/2019/2/6.

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39

Lodge, James P. "Materials Damage by Environmental Pollutants: Data Requirements." American Statistician 39, no. 4 (November 1985): 412. http://dx.doi.org/10.2307/2683022.

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40

Goben, Abigail, and Dorothea Salo. "Federal research: Data requirements set to change." College & Research Libraries News 74, no. 8 (September 1, 2013): 421–25. http://dx.doi.org/10.5860/crln.74.8.8994.

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41

Yu, Lina, Wanlin Gao, Qiong An, Jianing Zhao, Donghua Liang, and Dawei Gao. "Data resource management according to customer requirements." Mathematical and Computer Modelling 54, no. 3-4 (August 2011): 895–901. http://dx.doi.org/10.1016/j.mcm.2010.11.013.

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42

Muslah, Eman, and Said Ghoul. "Requirements Variability Specification for Data Intensive Software." International Journal of Software Engineering & Applications 10, no. 2 (March 31, 2019): 23–34. http://dx.doi.org/10.5121/ijsea.2019.10203.

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43

S. Al-kahtani, Mohammed. "Big Data Networking : Requirements, Architecture and Issues." International Journal of Wireless & Mobile Networks 8, no. 6 (December 30, 2016): 35–49. http://dx.doi.org/10.5121/ijwmn.2016.8604.

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44

Millar, T. J. "Chemistry in Dense Interstellar Clouds – Data Requirements." Highlights of Astronomy 8 (1989): 369–74. http://dx.doi.org/10.1017/s1539299600008005.

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ABSTRACTChemical models of dense interstellar clouds are reviewed with particular emphasis on recent results. The need for theoretical and experimental data on rate coefficients is pointed out and some observational studies are suggested.
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45

Espinosa, Amado L. "Availability of health data: requirements and solutions." International Journal of Medical Informatics 49, no. 1 (March 1998): 97–104. http://dx.doi.org/10.1016/s1386-5056(98)00016-1.

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46

Tate, Graham, June Verner, and Richard Hayward. "CASE Requirements for Data-Centred Business Applications." Journal of Information Technology 4, no. 4 (December 1989): 197–204. http://dx.doi.org/10.1057/jit.1989.34.

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47

Eastman, Peggy. "ACCC Meeting: Mounting Data Requirements Swamping Oncologists." Oncology Times 33, no. 14 (July 2011): 38–39. http://dx.doi.org/10.1097/01.cot.0000403394.55671.b8.

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48

Lodge, James P. "Materials Damage by Environmental Pollutants: Data Requirements." American Statistician 39, no. 4 (November 1985): 412–15. http://dx.doi.org/10.1080/00031305.1985.10479475.

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49

Aebersold, Ruedi. "Network Biology: Data Requirements and Biological Consequences." New Biotechnology 29 (September 2012): S1. http://dx.doi.org/10.1016/j.nbt.2012.08.095.

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50

Li, K., and J. Baillieul. "Data-rate requirements for nonlinear Feedback control." IFAC Proceedings Volumes 37, no. 13 (September 2004): 997–1002. http://dx.doi.org/10.1016/s1474-6670(17)31356-3.

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