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Libri sul tema "Creation of data models"

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Autore: Grafiati

Pubblicato: 17 luglio 2024

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1

Hughes, Barry. International futures: Choices in the creation of a new world order. 2^a ed. Boulder, Colo: Westview Press, 1996.

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2

Hughes, Barry. International futures: Choices in the creation of a new world order. Boulder, Colo: Westview Press, 1993.

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3

Rodríguez Bolívar, Manuel Pedro, Kelvin Joseph Bwalya e Christopher G. Reddick, a cura di. Governance Models for Creating Public Value in Open Data Initiatives. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14446-3.

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4

Hughes, Barry. International futures: Choices in the creation of a new world order. 2^a ed. Boulder, Colo: Westview Press, 1996.

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5

Grigorev, Anatoliy. Methods and algorithms of data processing. ru: INFRA-M Academic Publishing LLC., 2017. http://dx.doi.org/10.12737/22119.

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In this manual some methods and algorithms of data processing, the sequence of the solution of problems of processing and the analysis of data for creation of behavior model of an object taking into account all a component of his mathematical model are considered. Types of technological methods of use of software and hardware for the solution of tasks in this area are described. Algorithms of distributions, regressions of temporary ranks, their transformation for the purpose of receiving mathematical models and the forecast of behavior of information and economic systems (objects) are considered. Conforms to requirements of the Federal state educational standard of the higher education of the last generation. For students of economic specialties, experts, graduate students.

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6

Kaufmann, Manuel. Dreaming Data: Aspekte der Ästhetik, Originalität und Autorschaft in der künstlichen Kreativität. Zürich: Chronos, 2022.

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7

Boger, Dan C. On the feasibility of creating a comparable database for nonrecurring cost analysis under dual source competition. Monterey, Calif: Naval Postgraduate School, 1987.

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8

Kyper, Eric. Improved Decision-making in Data Mining: A Heuristic Rule Induction Approach to Decision Tree Creation and Model Selection. Saarbrücken: VDM Verlag Dr. Müller, 2008.

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9

Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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10

Winkelmann, Rainer. Count Data Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-21735-1.

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11

Pivert, Olivier, a cura di. NoSQL Data Models. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119528227.

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12

Thompson, J. Patrick. Data with semantics: Data models and data management. New York: Van Nostrand Reinhold, 1989.

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13

Veaux, Richard D. De. Stats: Data and models. 3^a ed. Boston: Pearson Education, 2012.

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14

Huang, Bert. Models for Sequence Data. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-14875-0.

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15

Olson, David L., e Desheng Wu. Predictive Data Mining Models. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9664-9.

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16

Cheeseman, P., e R. W. Oldford, a cura di. Selecting Models from Data. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2660-4.

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17

Olson, David L., e Desheng Wu. Predictive Data Mining Models. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2543-3.

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18

Veaux, Richard D. De. Stats: Data and models. 2^a ed. Boston: Pearson, Addison-Wesley, 2008.

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19

Veaux, Richard D. De. Stats: Data and models. Boston, MA: Pearson Learning Solutions, 2012.

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20

Brentari, Eugenio, Marcello Chiodi e Ernst-Jan Camiel Wit, a cura di. Models for Data Analysis. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15885-8.

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21

Weber, Alan. Data-driven business models. Mason, Ohio: Thomson, 2005.

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22

Marco, David. Universal meta data models. Indianapolis, Ind: Wiley, 2004.

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23

Zelterman, Daniel. Models for discrete data. Oxford: Clarendon Press, 1999.

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24

David, Benyon, Weedon Raymond A e Open University. Data Models and Databases Course Team., a cura di. Data models and databases. Milton Keynes: Open University, 1990.

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25

Veaux, Richard D. De. Stats: Data and models. Toronto: Pearson Canada, 2012.

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26

Marco, David. Universal Meta Data Models. New York: John Wiley & Sons, Ltd., 2004.

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27

Larose, Daniel T. Data Mining Methods and Models. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471756482.

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28

Stein, A., e F. W. T. Penning de Vries, a cura di. Data and Models in Action. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9217-8.

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29

Giudici, Paolo, Salvatore Ingrassia e Maurizio Vichi, a cura di. Statistical Models for Data Analysis. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00032-9.

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30

Elandt-Johnson, Regina C., e Norman L. Johnson. Survival Models and Data Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1999. http://dx.doi.org/10.1002/9781119011040.

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31

Congdon, Peter. Bayesian Models for Categorical Data. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470092394.

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32

Bearman, David. Archives & museum data models & dictionaries. Pittsburgh, Pa: Archives & Museum Informatics, 1990.

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33

Larose, Daniel T. Data Mining Methods and Models. New York: John Wiley & Sons, Ltd., 2006.

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34

Elandt-Johnson, Regina C. Survival models and data analysis. New York: Wiley, 1999.

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35

Cagetti, Marco. Wealth inequality: Data and models. Cambridge, Mass: National Bureau of Economic Research, 2006.

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36

Bearman, David. Archives & museum data models & dictionaries. Pittsburgh: Archives & Museum Informatics, 1990.

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37

Lee, Leo Taehyung. Instant Creating Data Models with PowerPivot How-To. Packt Publishing, Limited, 2013.

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38

Lee, Leo Taehyung. Instant Creating Data Models with PowerPivot How-To. Packt Publishing, Limited, 2013.

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39

Reddick, Christopher G., Manuel Pedro Rodríguez Bolívar e Kelvin Joseph Bwalya. Governance Models for Creating Public Value in Open Data Initiatives. Springer, 2019.

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40

Dreller, Anne. Creating Value from Data Sharing: Future-oriented Business Models in Theory and Practice. Springer Vieweg, 2018.

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41

Holt, Patrik, e Noel Williams. Computers and Writing: Models and Tools. Ablex Publishing, 1989.

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42

Powell, Brett. Microsoft Power BI Cookbook: Creating Business Intelligence Solutions of Analytical Data Models, Reports, and Dashboards. Packt Publishing, 2017.

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43

Data Models/conceptual Data Models: Study Units (Data Models and Databases). Open University Worldwide, 1990.

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44

Robinson, H. Data Models/facts, Dependencies and Data Models: Study Units (Data Models and Databases). Open University Worldwide, 1990.

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45

Sundren, Bo. Data Bases and Data Models. Chartwell-Bratt Ltd, 1985.

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46

Olson, David. Data Mining Models. Business Expert Press, 2016.

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47

Pdxi Data Models. Amer Inst of Chemical Engineers, 1998.

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48

Olson, David. Data Mining Models. Business Expert Press, 2016.

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49

Olson, David L. Data Mining Models. Business Expert Press, 2018.

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50

Olson, David L. Data Mining Models. Business Expert Press, 2016.

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