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Books on the topic 'Interactional data'

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

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1

Cao, Longbing, Yifeng Zeng, Andreas L. Symeonidis, Vladimir Gorodetsky, Jörg P. Müller, and Philip S. Yu, eds. Agents and Data Mining Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55192-5.

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2

Cao, Longbing, Yifeng Zeng, Bo An, Andreas L. Symeonidis, Vladimir Gorodetsky, Frans Coenen, and Philip S. Yu, eds. Agents and Data Mining Interaction. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20230-3.

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3

Cao, Longbing, Vladimir Gorodetsky, Jiming Liu, Gerhard Weiss, and Philip S. Yu, eds. Agents and Data Mining Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03603-3.

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4

Cao, Longbing, Yifeng Zeng, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Philip S. Yu, and Munindar P. Singh, eds. Agents and Data Mining Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36288-0.

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5

Cao, Longbing, Ana L. C. Bazzan, Vladimir Gorodetsky, Pericles A. Mitkas, Gerhard Weiss, and Philip S. Yu, eds. Agents and Data Mining Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15420-1.

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6

Cao, Longbing, Ana L. C. Bazzan, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Gerhard Weiss, and Philip S. Yu, eds. Agents and Data Mining Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27609-5.

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7

Cannataro, Mario, and Pietro Hiram Guzzi. Data Management of Protein Interaction Networks. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118103746.

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8

1980-, Guzzi Pietro Hiram, ed. Data management of protein interaction networks. Hoboken, NY: Wiley, 2012.

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9

Oberholzner, Werner. SWADE data guide. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1996.

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10

Mulvihill, C. G. Group interaction support. Dublin: University College Dublin, 1996.

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11

Arbib, Michael A., and Shun-ichi Amari, eds. Dynamic Interactions in Neural Networks: Models and Data. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-4536-0.

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12

Cervone, H. Frank, and Lars G. Svensson. Linked data and user interaction: The road ahead. Berlin: De Gruyter Saur, 2015.

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13

International Atomic Energy Agency; IAEA. Atomic and plasma-material interaction data for fusion. Vienna: IAEA, 1995.

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14

JSC/CCCO Working Group on Air-sea Fluxes. Global data assimilation programme for air-sea fluxes. [Geneva]: World Meteorological Organization, 1988.

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15

Aseev, G. G. Electrolytes, interparticle interactions: Theory, calculation methods, and experimental data. New York: Begell House, 1998.

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16

Data access patterns: Database interactions in object-oriented applications. Boston: Addison-Wesley, 2004.

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17

Dunagan, Stephen E. Interferometric data for a shock-wave/boundary-layer interaction. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1986.

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18

Dunagan, Stephen E. Interferometric data for a shock-wave/boundary-layer interaction. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1986.

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19

Moran, Thomas P. Human-computer interaction. Mahwah, N.J: Lawrence Erlbaum, 2000.

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20

1926-, Griffith H. Winter, ed. Drug interactions: IBM PC version. 2nd ed. Phila. PA: Saunders Software, 1986.

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21

High-level data fusion. Boston: Artech House, 2008.

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22

Interpreting qualitative data: Methods for analysing talk, text, and interaction. 2nd ed. London: Sage Publications, 2001.

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23

David, Silverman. Interpreting qualitative data: Methods for analysing talk, text, and interaction. London: Sage Publications, 1993.

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24

Stillwell, John C. H. Technologies for migration and commuting analysis: Spatial interaction data applications. Hershey, PA: Business Science Reference, 2010.

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25

Holloway, P. J. Interactions Between Adjuvants, Agrochemicals and Target Organisms. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994.

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26

Discrete structures and their interactions. Boca Raton: CRC Press/Taylor & Francis Group, 2013.

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27

Understanding user-Web interactions via Web analytics. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2009.

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28

Stillwell, John C. H. The development of a web-based interface to census interaction data. Leeds: School of Geography, University of Leeds, 2000.

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29

Reddy, Francis. Chance discoveries in real world decision making: Data-based interaction of. [S.l.]: Springer, 2010.

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30

Holzinger, Andreas, and Gabriella Pasi, eds. Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39146-0.

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31

Transcribing talk and interaction: Issues in the representation of communication data. Amsterdam: John Benjamins Pub. Co., 2011.

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32

Wilson, John W. Nucleon-nucleus interaction data base: Total nuclear and absorption cross sections. Hampton, Va: Langley Research Center, 1988.

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33

ADMI 2010 (2010 Toronto, Ont.). Agents and data mining interaction: 6th International Workshop on Agents and Data Mining Interaction, ADMI 2010, Toronto, On, Canada, May 11, 2010 : revised selected papers. Berlin: Springer, 2010.

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34

Reichmann, Werner. The Interactional Foundations of Economic Forecasting. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198820802.003.0005.

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How do economic forecasters produce legitimate and credible predictions of the economic future, despite most of the economy being transmutable and indeterminate? Using data from a case study of economic forecasting institutes in Germany, this chapter argues that the production of credible economic futures depends on an epistemic process embedded in various forms of interaction. This interactional foundation—through ‘foretalk’ and ‘epistemic participation’ in networks of internal and external interlocutors—sharpens economic forecasts in three ways. First, it brings to light new imaginaries of the economic future, allowing forecasters to spot emerging developments they would otherwise have missed. Second, it ensures the forecasts’ social legitimacy. And finally, it increases the forecasts’ epistemic quality by providing decentralized information about the intentions and assumptions of key economic and political actors.
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35

Clavel, Chloe. Opinion Analysis in Interactions: From Data Mining to Human-Agent Interaction. Wiley & Sons, Incorporated, John, 2019.

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36

Clavel, Chloe. Opinion Analysis in Interactions: From Data Mining to Human-Agent Interaction. Wiley & Sons, Incorporated, John, 2019.

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37

Clavel, Chloe. Opinion Analysis in Interactions: From Data Mining to Human-Agent Interaction. Wiley & Sons, Incorporated, John, 2019.

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38

Svensson, Lars, H. Frank Cervone, and Alenka Kavcic-Colic. Linked Data and User Interaction. De Gruyter, Inc., 2014.

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39

Ana L. C. Bazzan,Vladimir Gorodetsky,Pericles A. Mitkas. Agents and Data Mining Interaction. Springer, 2011.

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40

Longbing Cao,Jiming Liu,A. E. Gorodetsky. Agents and Data Mining Interaction. Springer, 2009.

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41

Cervone, H. Frank, and Lars G. Svensson, eds. Linked Data and User Interaction. De Gruyter Saur, 2015. http://dx.doi.org/10.1515/9783110317008.

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42

Risman, Barbara J. Getting the Stories. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199324385.003.0004.

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This chapter describes the qualitative methodology used in this study. Risman and her students and colleagues designed an interview schedule to study gender as a social structure. Questions were asked about experiences across different life contexts. Questions focused on the individual level of identities, the interactional level of expectations they held for others and faced by themselves, and their macro-level ideologies and experiences of institutional constraints. Most of the 116 respondents were from Chicagoland and were recruited at local universities, LGBTQ centers, and by word of mouth. The majority‒minority sample was also gender diverse including transgender, genderqueer, and other nonconforming respondents. All data were recorded and transcribed for qualitative data analysis. A preview of the findings is included as a conclusion to the chapter.
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43

Cannataro, Mario, and Pietro H. Guzzi. Data Management of Protein Interaction Networks. IEEE Computer Society Press, 2012.

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44

Cannataro, Mario, and Pietro H. Guzzi. Data Management of Protein Interaction Networks. IEEE Computer Society Press, 2012.

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45

Morrow, James D. The Interaction of Theory and Data. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190846626.013.334.

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Theory shapes how data is collected and analyzed in at least three ways. Theoretical concepts inform how we collect data because data attempt to capture and reflect those concepts. Theory provides testable hypotheses that direct our research. Theory also helps us draw conclusions from the results of empirical research. Meanwhile, research using quantitative methods seeks to be rigorous and reproducible. Mathematical models develop the logic of a theory carefully, while statistical methods help us judge whether the evidence matches the expectations of our theories. Quantitative scholars tend to specialize in one approach or the other. The interaction of theory and data for them thus concerns how models and statistical analysis draw on and respond to one another. In the abstract, they work together seamlessly to advance scientific understanding. In practice, however, there are many places and ways this abstract process can stumble. These difficulties are not unique to rigorous methods; they confront any attempt to reconcile causal arguments with reality. Rigorous methods help by making the issues clear and forcing us to confront them. Furthermore, these methods do not ensure arguments or empirical judgments are correct; they only make it easier for us to agree among ourselves when they do.
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46

Hitlin, Steven, and Sarah K. Harkness. Conclusion. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190465407.003.0010.

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The book concludes with this chapter summarizing the general argument, hopefully more compelling in light of our empirical support. Ultimately, we call for researchers to adopt alternate empirical approaches to test our argument that societal inequality is linked to culturally shaped moral codes and emotions. Gathering the data within a country to assess these interactional moral processes is almost prohibitively expensive, so it is difficult to replicate our ideas within a wider array of countries and methodological techniques. However, if this theory has merit, we hope others will pick up on the broad links between macro and micro to refine, or argue with, this admittedly ambitious thesis.
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47

1969-, Li Xiao-Li, and Ng See-Kiong, eds. Biological data mining in protein interaction networks. Hershey, PA: Medical Information Science Reference, 2009.

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48

McVittie, Chris, and Andy McKinlay. Collaborative Processes in Neuropsychological Interviews. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198737865.003.0012.

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Acts of remembering are often social events and not entirely internal matters. Yet typically neuropsychological interviews are treated as a means of assessing cognitive abilities and impairments of the individual patient with little attention paid to the context within which the patient produces or fails to produce an act of remembering. Using discourse analysis, we examine data from interviews with patients diagnosed as having two forms of memory impairment, namely dense amnesia and momentary confabulation. Outcomes of memory failure on the tasks set are seen to be joint achievements of interviewers and interviewees. Interviewers are not passive in these encounters but active participants. Their contributions, along with patients’ descriptions and other interactional elements, comprise collaborative processes in neuropsychological interviews.
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49

Mark, Donelan, Wallops Flight Facility, and United States. Office of Naval Research, eds. SWADE data guide. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1996.

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50

Dynamic Interactions in Neural Networks: Models and Data. Springer, 2011.

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