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Zeitschriftenartikel zum Thema „Data management“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Mai 2024

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1

Moreno, P., M. Ruiz und F. J. Gorines. „TBM Process Data Management System“. International Journal of Engineering and Technology 7, Nr. 5 (Dezember 2015): 431–34. http://dx.doi.org/10.7763/ijet.2015.v7.832.

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2

Sashi, K., und Antony Selvadoss Thanamani. „Dynamic Replica Management for Data Grid“. International Journal of Engineering and Technology 2, Nr. 4 (2010): 329–33. http://dx.doi.org/10.7763/ijet.2010.v2.142.

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3

Lee, Jonghyun Harry, Tyler Hesser, Matthew Farthing, Spicer Bak und Katherine DeVore. „SCALABLE REAL-TIME DATA ASSIMILATION WITH VARIOUS DATA TYPES FOR ACCURATE SPATIOTEMPORAL NEARSHORE BATHYMETRY ESTIMATION“. Coastal Engineering Proceedings, Nr. 37 (02.10.2023): 156. http://dx.doi.org/10.9753/icce.v37.management.156.

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Immediate estimation of nearshore bathymetry is crucial for accurate prediction of nearshore wave conditions and coastal flooding events. However, direct bathymetry data collection is expensive and time-consuming, while accurate airborne lidar-based survey is limited by breaking waves and decreased light penetration affected by water turbidity. Several recent efforts have been made to apply interpolation and inverse modeling approaches to indirect remote sensed observations along with sparse direct survey data points. Example indirect observations include video-based observations such as time-series snapshots and time-averaged (Timex) images across the surf zone taken from tower-based platforms and Unmanned Aircraft Systems (UASs), while stationary LiDAR tower and UAS flights with infrared camera capability or imagery-based structure-from-motion (SfM) algorithms have been used to provide beach topographic data. In this work, we present three bathymetry estimation tools for real-time nearshore characterization using different types of information.

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4

Meseck, Reed M. „Data management“. ACM SIGMOD Record 30, Nr. 2 (Juni 2001): 569–70. http://dx.doi.org/10.1145/376284.375745.

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5

Thompson, Cheryl Bagley, und Edward A. Panacek. „Data Management“. Air Medical Journal 27, Nr. 4 (Juli 2008): 156–58. http://dx.doi.org/10.1016/j.amj.2008.05.001.

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6

Marchant, David. „Data Management“. Museum Management and Curatorship 18, Nr. 2 (Januar 1999): 197–201. http://dx.doi.org/10.1080/09647779900801802.

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7

Yasmeen, Mrs. „NOSQL Database Engines for Big Data Management“. International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (31.10.2018): 617–22. http://dx.doi.org/10.31142/ijtsrd18608.

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8

McDonald, John. „Records Management and Data Management“. Records Management Journal 1, Nr. 1 (Januar 1989): 4–11. http://dx.doi.org/10.1108/eb027016.

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9

Lagoze, Carl, William C. Block, Jeremy Williams, John Abowd und Lars Vilhuber. „Data Management of Confidential Data“. International Journal of Digital Curation 8, Nr. 1 (14.06.2013): 265–78. http://dx.doi.org/10.2218/ijdc.v8i1.259.

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Social science researchers increasingly make use of data that is confidential because it contains linkages to the identities of people, corporations, etc. The value of this data lies in the ability to join the identifiable entities with external data, such as genome data, geospatial information, and the like. However, the confidentiality of this data is a barrier to its utility and curation, making it difficult to fulfil US federal data management mandates and interfering with basic scholarly practices, such as validation and reuse of existing results. We describe the complexity of the relationships among data that span a public and private divide. We then describe our work on the CED2AR prototype, a first step in providing researchers with a tool that spans this divide and makes it possible for them to search, access and cite such data.

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10

Aiken, Peter, Mark Gillenson, Xihui Zhang und David Rafner. „Data Management and Data Administration“. Journal of Database Management 22, Nr. 3 (Juli 2011): 24–45. http://dx.doi.org/10.4018/jdm.2011070102.

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Data management (DM) has existed in conjunction with software development and the management of the full set of information technology (IT)-related components. However, it has been more than two decades since research into DM as it is practiced has been published. In this paper, the authors compare aspects of DM across a quarter-century timeline, obtaining data using comparable sets of subject matter experts. Using this information to observe the profession’s evolution, the authors have updated the understanding of DM as it is practiced, giving additional insight into DM, including its current responsibilities, reporting structures, and perceptions of success, among other factors. The analysis indicates that successfully investing in DM presents current, real challenges to IT and organizations. Although DM is evolving away from purely operational responsibilities toward higher-level responsibilities, perceptions of success have fallen. This paper details the quarter-century comparison of DM practices, analyzes them, and draws conclusions.

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11

Seegers, G., H. Kempff und C. J. Van Rees. „Data management and data analysis“. Poetics 16, Nr. 6 (Dezember 1987): 535–52. http://dx.doi.org/10.1016/0304-422x(87)90016-7.

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12

Bragina, D., und N. Molodchik. „BIG DATA IN HUMAN RESOURCE MANAGEMENT“. Management of the Personnel and Intellectual Resources in Russia 9, Nr. 3 (23.07.2020): 76–80. http://dx.doi.org/10.12737/2305-7807-2020-76-80.

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The article discusses the possibilities of using big data in the field of human resources management, shows the difficulties that can be encountered when introducing these technologies into the work of the company. The main problems of the use of employee data by companies for the analysis, forecasting and improvement production indicators are given. Examples of companies that successfully use big data in their work are shown. Recommendations how to introduce the technology of big data analysis in the field of human resource management are given.

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13

Abdalla, Hemn B., Jinzhao Lin, Guoquang Li und S. Mushhad M. Gilani. „NoSQL: Confidential on Data Security and Data Management by Using a Mobile Application“. International Journal of Information and Electronics Engineering 6, Nr. 2 (2016): 84–88. http://dx.doi.org/10.18178/ijiee.2016.6.2.600.

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14

Tiwari, Archita. „A Critical Analysis on Health Data Management Policy“. International Journal of Science and Research (IJSR) 12, Nr. 12 (05.12.2023): 488–91. http://dx.doi.org/10.21275/sr231205224541.

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15

Soltau, Felix, Sebastian Niehüser und Jürgen Jensen. „CHALLENGES IN AUTOMATION OF QUALITY CONTROL FOR TIDE GAUGE DATA“. Coastal Engineering Proceedings, Nr. 37 (02.10.2023): 169. http://dx.doi.org/10.9753/icce.v37.management.169.

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Tide gauges provide important water level data for navigation, port management, coastal protection strategies, ecological adaptation measures, or climate change assessments. For these tasks, a reliable availability and high quality of the data is crucial. However, water level data from tide gauges contain technical errors as well as anthropogenic and natural influences. For the German North Sea coast and estuaries, resulting water level anomalies are partially detected and corrected manually by qualified personnel and further considered by individual subsequent users of that data. Figure 1 shows an example of such a correction of water level anomalies around tidal low water from tide gauge data at Husum, Germany, in 2016. In general, manual quality control leads to different handlings and thus incomparable results. Consequently, a uniform and automated pre-processing is needed for tide gauge data in Germany in order to detect, correct, and classify anomalies ideally in real time. The developed pre-processing approaches will not be limited to tide gauges in Germany but can be globally transferred or be extended to river sites.

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16

Kiehn, Felix, Mareike Schmidt, Daniel Glake, Fabian Panse, Wolfram Wingerath, Benjamin Wollmer, Martin Poppinga und Norbert Ritter. „Polyglot data management“. Proceedings of the VLDB Endowment 15, Nr. 12 (August 2022): 3750–53. http://dx.doi.org/10.14778/3554821.3554891.

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Due to the increasing variety of the current database landscape, polyglot data management has become a hot research topic in recent years. The underlying idea is to combine the benefits of different data stores behind a predefined set of common interfaces and thus address use cases that individual stores cannot meet. This can be accomplished using different approaches which vary greatly in terms of capabilities, functionality, and architectural concepts. This tutorial provides a detailed overview of the current state of research in polyglot data management. We motivate its use by showing the high diversity of existing data stores and discussing three use cases in which individual stores are insufficient. Thereafter, we present different taxonomies for classifying polyglot data systems and give a detailed review of a number of selected systems. Finally, we compare these systems based on their features and discuss open challenges that still need to be addressed in future research.

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17

Surkis, Alisa, und Kevin Read. „Research data management“. Journal of the Medical Library Association : JMLA 103, Nr. 3 (Juli 2015): 154–56. http://dx.doi.org/10.3163/1536-5050.103.3.011.

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18

Kasemsap, Kijpokin. „Big Data Management“. International Journal of Organizational and Collective Intelligence 7, Nr. 3 (Juli 2017): 44–55. http://dx.doi.org/10.4018/ijoci.2017070104.

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The objective of this article is to provide the advanced issues and approaches of big data management. The literature review indicates the overview of big data management; the aspects of Big Data Analytics (BDA); the importance of big data management; the methods for big data management; the privacy and security concerns of big data management; and the big data management in the health care industry. Organizations that have been successful in working with effective big data management have accomplished this issue using data to help make sense of the information. The volume of data that companies are able to gather about customers and market conditions can provide business leaders with insights into new revenue and business opportunities, presuming they can spot the opportunities in vast amounts of data. The literature review analysis provides both practitioners and researchers an important understanding about big data management in modern organizations.

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19

Denney, Dennis. „Subsea-Data Management“. Journal of Petroleum Technology 59, Nr. 12 (01.12.2007): 75–77. http://dx.doi.org/10.2118/1207-0075-jpt.

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20

YU, Min. „P2P Data Management“. Journal of Software 17, Nr. 8 (2006): 1717. http://dx.doi.org/10.1360/jos171717.

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21

Jensen, C. S., und R. T. Snodgrass. „Temporal data management“. IEEE Transactions on Knowledge and Data Engineering 11, Nr. 1 (1999): 36–44. http://dx.doi.org/10.1109/69.755613.

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22

Heuer, Andreas. „Research Data Management“. it - Information Technology 62, Nr. 1 (25.02.2020): 1–5. http://dx.doi.org/10.1515/itit-2020-0002.

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23

Madria, S. K., und S. S. Bhowdrick. „Mobile data management“. IEEE Potentials 20, Nr. 4 (2001): 11–15. http://dx.doi.org/10.1109/45.969587.

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24

Hoehl, Karl, Günther Merkl, Uwe Gerberding, Norbert Singer, Markus Schnee und Gabriele Kille. „Engineering Data Management“. ATZextra worldwide 14, Nr. 1 (Januar 2009): 156–61. http://dx.doi.org/10.1365/s40111-009-0148-4.

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25

Nargesian, Fatemeh, Erkang Zhu, Renée J. Miller, Ken Q. Pu und Patricia C. Arocena. „Data lake management“. Proceedings of the VLDB Endowment 12, Nr. 12 (August 2019): 1986–89. http://dx.doi.org/10.14778/3352063.3352116.

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26

Meliou, Alexandra, Wolfgang Gatterbauer und Dan Suciu. „Reverse data management“. Proceedings of the VLDB Endowment 4, Nr. 12 (August 2011): 1490–93. http://dx.doi.org/10.14778/3402755.3402803.

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27

Stoyanovich, Julia, Bill Howe und H. V. Jagadish. „Responsible data management“. Proceedings of the VLDB Endowment 13, Nr. 12 (August 2020): 3474–88. http://dx.doi.org/10.14778/3415478.3415570.

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28

Troisi, Orlando, Mario D’Arco, Francesca Loia und Gennaro Maione. „Big data management“. International Journal of Engineering Business Management 10 (Januar 2018): 184797901876777. http://dx.doi.org/10.1177/1847979018767776.

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29

Mokbel, Mohamed, Chi-Yin Chow und Walid Aref. „Location data management“. Proceedings of the VLDB Endowment 9, Nr. 13 (September 2016): 1622. http://dx.doi.org/10.14778/3007263.3007327.

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30

Palpanas, Themis. „Data Series Management“. ACM SIGMOD Record 44, Nr. 2 (12.08.2015): 47–52. http://dx.doi.org/10.1145/2814710.2814719.

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31

Kannan, K. L., und L. R. Baylor. „ATF data management“. Review of Scientific Instruments 59, Nr. 8 (August 1988): 1783–85. http://dx.doi.org/10.1063/1.1140111.

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32

Khurana, Purnima, und Purnima Bindal. „Test Data Management“. International Journal of Computer Trends and Technology 15, Nr. 4 (25.09.2014): 162–67. http://dx.doi.org/10.14445/22312803/ijctt-v15p135.

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33

Clarke, Drew. „Antarctic data management“. Antarctic Science 5, Nr. 3 (September 1993): 237. http://dx.doi.org/10.1017/s0954102093000318.

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Antarctic science is inter-disciplinary in character, multi-national in execution, and globally relevant. Data management in this environment must be examined from political, scientific and economic perspectives. The Antarctic Treaty calls on parties to exchange and make freely available scientific observations and results from Antarctica, so establishing the political context for addressing data management. The scientific context arises from the increasingly large and complex issues being addressed, including environmental monitoring and global change programmes, while the economic context considers data and information as the primary assets derived from Antarctic expenditure.

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34

Kaku, Kazuya, Takashi Yamazaki, Toshiaki Hashimoto und Ryo Tanabe. „Data management/system“. Geocarto International 12, Nr. 4 (Dezember 1997): 79–85. http://dx.doi.org/10.1080/10106049709354620.

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35

Ausman, R. K., G. D. Baer, M. R. McGuire, R. A. Marks, R. Ewart, J. Carey und R. L. West. „CLINICAL DATA MANAGEMENT*“. Annals of the New York Academy of Sciences 128, Nr. 3 (16.12.2006): 1100–1107. http://dx.doi.org/10.1111/j.1749-6632.1965.tb11719.x.

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36

Hawkins, Barbara S. „Clinical data management“. Controlled Clinical Trials 16, Nr. 6 (Dezember 1995): 458–59. http://dx.doi.org/10.1016/s0197-2456(95)00025-9.

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37

Sheth, S. „Engineering Data Management“. IFAC Proceedings Volumes 25, Nr. 7 (Mai 1992): 279–83. http://dx.doi.org/10.1016/s1474-6670(17)52377-0.

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38

MOCHIZUKI, Tatsuya. „Product Data Management“. Journal of the Japan Society for Precision Engineering 72, Nr. 2 (2006): 161–64. http://dx.doi.org/10.2493/jjspe.72.161.

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39

Golab, Lukasz, und M. Tamer Özsu. „Data Stream Management“. Synthesis Lectures on Data Management 2, Nr. 1 (Januar 2010): 1–73. http://dx.doi.org/10.2200/s00284ed1v01y201006dtm005.

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40

Mamoulis, Nikos. „Spatial Data Management“. Synthesis Lectures on Data Management 3, Nr. 6 (30.11.2011): 1–149. http://dx.doi.org/10.2200/s00394ed1v01y201111dtm021.

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41

Kirsch, Ted M., Maarja Hildebrand, Mirza Beg und Michael J. Fox. „Ancillary Data Management“. Drug Information Journal 22, Nr. 1 (Januar 1988): 99–101. http://dx.doi.org/10.1177/009286158802200115.

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42

Orike, Sunny, und Daboso Brown. „Big Data Management“. International Journal of Interdisciplinary Telecommunications and Networking 8, Nr. 4 (Oktober 2016): 34–50. http://dx.doi.org/10.4018/ijitn.2016100104.

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Organizations and governments leverage on the potentials in data to plan and compete globally. Data from various sources are continually mined, stored in databases and utilized in a manner that improves processes, products and ensure steady profitability. The traditional relational database management systems are unable to cope with these new forms of data. The velocity, volume and variety in which data are generated qualify them as “Big Data”. This scales up the storage and processing needs of client organizations, allowing them to focus on their core areas of expertise. This paper investigates the relevance of aggregating big data and its efficient management in a cloud computing environment. It adopts the prototyping design and a case study approach, based on Couchbase Server, a non-structured query language document database and cloud computing platform. The accruable benefit of this work is organizational cost-effectiveness, timeliness in decision making and higher profitability.

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43

Merritt, W. T. „ANESTHESIA DATA MANAGEMENT“. Anesthesiology 81, SUPPLEMENT (September 1994): A538. http://dx.doi.org/10.1097/00000542-199409001-00537.

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44

Wygant, Robert M. „Data file management“. Computers & Industrial Engineering 11, Nr. 1-4 (Januar 1986): 367–71. http://dx.doi.org/10.1016/0360-8352(86)90113-0.

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45

Stonier, P. D. „Clinical data management“. Human Psychopharmacology: Clinical and Experimental 16, Nr. 4 (2001): 360–61. http://dx.doi.org/10.1002/hup.285.

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46

Schäffer, Utz. „Data Management 2.0“. Controlling & Management Review 67, Nr. 4 (August 2023): 3. http://dx.doi.org/10.1007/s12176-023-1054-8.

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47

Demartini, Gianluca, Kevin Roitero und Stefano Mizzaro. „Data Bias Management“. Communications of the ACM 67, Nr. 1 (21.12.2023): 28–32. http://dx.doi.org/10.1145/3611641.

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48

Limaye, Nimita. „Data management redefined“. Perspectives in Clinical Research 1, Nr. 3 (2010): 110. http://dx.doi.org/10.4103/2229-3485.71868.

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49

Miksa, Tomasz, Simon Oblasser und Andreas Rauber. „Automating Research Data Management Using Machine-Actionable Data Management Plans“. ACM Transactions on Management Information Systems 13, Nr. 2 (30.06.2022): 1–22. http://dx.doi.org/10.1145/3490396.

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Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks. In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs.

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50

Zakharova, Irina G. „Big Data and Educational Process Management“. Tyumen State University Herald. Humanities Research. Humanitates 3, Nr. 1 (2017): 210–19. http://dx.doi.org/10.21684/2411-197x-2017-3-1-210-219.

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