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Books on the topic 'Data integration'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 September 2024

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1

Genesereth, Michael. Data Integration. Cham: Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01550-2.

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2

Dyché, Jill, and Evan Levy, eds. Customer Data Integration. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119202127.

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3

Majkić, Zoran. Big Data Integration Theory. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04156-8.

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4

Doan, AnHai. Principles of data integration. Waltham, MA: Morgan Kaufmann, 2012.

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5

Goldfedder, Jarrett. Building a Data Integration Team. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5653-4.

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6

Davino, Cristina, and Luigi Fabbris, eds. Survey Data Collection and Integration. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-21308-3.

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7

Viola de Azevedo Cunha, Mario. Market Integration Through Data Protection. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6085-1.

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8

Kerr, W. Scott. Data integration using virtual repositories. [Toronto]: Kerr, 1999.

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9

Ning, Kang, ed. Methodologies of Multi-Omics Data Integration and Data Mining. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1.

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10

Office, United States Department of the Interior Bureau of Land Management ALMRS Project. Bureau of Land Management data integration. [Denver, Colo: Bureau of Land Management, ALMRS Project Office, 1985.

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11

Auer, Sören, and Maria-Esther Vidal, eds. Data Integration in the Life Sciences. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06016-9.

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12

Kutsche, Ralf-Detlef, and Nikola Milanovic, eds. Model-Based Software and Data Integration. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78999-4.

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13

Woon, Wei Lee, Zeyar Aung, and Stuart Madnick, eds. Data Analytics for Renewable Energy Integration. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13290-7.

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14

Paton, Norman W., Paolo Missier, and Cornelia Hedeler, eds. Data Integration in the Life Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02879-3.

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15

Cohen-Boulakia, Sarah, and Val Tannen, eds. Data Integration in the Life Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73255-6.

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16

Ludäscher, Bertram, and Louiqa Raschid, eds. Data Integration in the Life Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11530084.

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17

Leser, Ulf, Felix Naumann, and Barbara Eckman, eds. Data Integration in the Life Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11799511.

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18

Baker, Christopher J. O., Greg Butler, and Igor Jurisica, eds. Data Integration in the Life Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39437-9.

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19

Da Silveira, Marcos, Cédric Pruski, and Reinhard Schneider, eds. Data Integration in the Life Sciences. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69751-2.

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20

Woon, Wei Lee, Zeyar Aung, Oliver Kramer, and Stuart Madnick, eds. Data Analytics for Renewable Energy Integration. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50947-1.

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21

Aspin, Adam. SQL Server 2012 Data Integration Recipes. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4792-0.

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22

Woon, Wei Lee, Zeyar Aung, and Stuart Madnick, eds. Data Analytics for Renewable Energy Integration. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27430-0.

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23

Cao, Longbing, ed. Data Mining and Multi-agent Integration. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0522-2.

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24

Ashish, Naveen, and Jose-Luis Ambite, eds. Data Integration in the Life Sciences. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21843-4.

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25

Galhardas, Helena, and Erhard Rahm, eds. Data Integration in the Life Sciences. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08590-6.

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26

Rahm, Erhard, ed. Data Integration in the Life Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b96666.

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27

Bairoch, Amos, Sarah Cohen-Boulakia, and Christine Froidevaux, eds. Data Integration in the Life Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69828-9.

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28

Lambrix, Patrick, and Graham Kemp, eds. Data Integration in the Life Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15120-0.

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29

Bodenreider, Olivier, and Bastien Rance, eds. Data Integration in the Life Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31040-9.

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30

Katherine, Martel, and AWWA Research Foundation, eds. Data integration for water quality management. Denver, Colo: Awwa Research Foundation, 2005.

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31

Ikeda, Yuichi. Data Science of Renewable Energy Integration. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8779-5.

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32

service), SpringerLink (Online, ed. Data Mining and Multi-agent Integration. Boston, MA: Springer-Verlag US, 2009.

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33

Lohse, Anja. Integration unterschiedlich strukturierter Daten. Lohmar: J. Eul, 2003.

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34

Alper, Joe, ed. Informing Environmental Health Decisions Through Data Integration. Washington, D.C.: National Academies Press, 2018. http://dx.doi.org/10.17226/25139.

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35

Leonard, Andy. Data Integration Life Cycle Management with SSIS. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3276-7.

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36

Institute, SAS. SAS Data Integration Studio 4.21: User's guide. Cary, N.C: SAS Institute, 2009.

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37

National Institute of Standards and Technology (U.S.), ed. Management data specification for supply chain integration. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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38

Commission, European, and Statistical Office of the European Communities, eds. Integration - indicators for energy: Data 1985-98. 2nd ed. Luxembourg: Office for Official Publications of the European Communities, 2001.

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39

Robert, Eve, ed. Data virtualization: Going beyond traditional data integration to achieve business agility. [Place of publication not identified]: Nine Five One Press, 2011.

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40

Srivastava, Divesh, and Xin Luna Dong. Big Data Integration. Springer International Publishing AG, 2015.

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41

Big Data Integration. Springer Nature, 2015.

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42

Angell, Robert. Enterprise Data Integration. Pearson Education, Limited, 1998.

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43

Levy, Evan, and Jill Dyche. Customer Data Integration. Wiley & Sons, Incorporated, John, 2006.

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44

Voice & Data Integration. Mc Graw Hill's Primis Custom Publishing, 2005.

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45

Customer Data Integration. New York: John Wiley & Sons, Ltd., 2006.

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46

Srivastava, Divesh, and Xin Luna Dong. Big Data Integration. Morgan & Claypool Publishers, 2015.

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47

Srivastava, Divesh, and Xin Luna Dong. Big Data Integration. Morgan & Claypool Publishers, 2015.

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48

Hanck, Patrick, Scott Hertel, Allen Lissaraggue, Bing Chen, and James Hanck. Data Integration with SAP Data Services. Rheinwerk Publishing Inc., 2015.

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49

Principles of Data Integration. Elsevier, 2012. http://dx.doi.org/10.1016/c2011-0-06130-6.

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50

Razo, Armando. Integration of Contextual Data. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.20.

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Abstract:
This chapter discusses a conceptual framework that clarifies the nature and importance of context in social scientific research. It first explains how context fits into survey analysis, then addresses major problems that hamper use and collection of contextual data: vague or incomplete conceptual definitions of “context” and lack of methodological guidance to collect and analyze contextual data. It suggests that systematic research and cumulative knowledge on contextual effects are constrained by two factors: the lack of standardized contextual variables across surveys and sporadic empirical inquiries. Finally, it outlines directions for future research with an eye toward advancing contextual data collection and analysis as well as ascertaining the impact of context on public opinion and political behavior. It presents statistical approaches to provide a blueprint for explicit measurements and analysis of contextual data and considers the need to modify conventional sampling techniques to capture relevant contextual variability.
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