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1. |
Record Nr. |
UNISA996466273703316 |
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Titolo |
Advances in Conceptual Modeling [[electronic resource] ] : ER 2019 Workshops FAIR, MREBA, EmpER, MoBiD, OntoCom, and ER Doctoral Symposium Papers, Salvador, Brazil, November 4–7, 2019, Proceedings / / edited by Giancarlo Guizzardi, Frederik Gailly, Rita Suzana Pitangueira Maciel |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (279 pages) |
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Collana |
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Programming and Software Engineering ; ; 11787 |
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Disciplina |
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Soggetti |
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Software engineering |
Algorithms |
Mathematical logic |
Application software |
Database management |
Software Engineering |
Algorithm Analysis and Problem Complexity |
Mathematical Logic and Formal Languages |
Computer Appl. in Administrative Data Processing |
Database Management |
Information Systems Applications (incl. Internet) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Conceptual Modeling, Ontologies and Metadata Management for FAIR Data -- Conceptual Modeling in Requirements Engineering and Business Analysis -- Empirical Methods in Conceptual Modeling -- Modeling and Management of Big Data -- Ontologies and Conceptual Modelling. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of five workshops symposia, held at the 38th International Conference on Conceptual Modeling, ER 2019, in Salvador, Brazil, in November 2019. The 34 |
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papers promote and disseminate research on theories of concepts underlying conceptual modeling, methods and tools for developing and communicating conceptual models, techniques for transforming conceptual models into effective implementations, and the impact of conceptual modeling techniques on databases, business strategies and information systems. The following workshops are included in this volume: Workshop on Conceptual Modeling, Ontologies and Metadata Management for FAIR Data (FAIR), 6th Workshop on Conceptual Modeling in Requirements Engineering and Business Analysis (MREBA), 2nd International Workshop on Empirical Methods in Conceptual Modeling (EmpER), 8th International Workshop on Modeling and Management of Big Data (MoBiD19), and 7th International Workshop on Ontologies andConceptual Modelling (OntoCom). |
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2. |
Record Nr. |
UNINA9910346735603321 |
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Autore |
Magda Osman |
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Titolo |
Complex Problem Solving Beyond the Psychometric Approach |
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Pubbl/distr/stampa |
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Descrizione fisica |
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1 online resource (178 p.) |
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Collana |
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Frontiers Research Topics |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Complex problem solving (CPS) and related topics such as dynamic decision-making (DDM) and complex dynamic control (CDC) represent multifaceted psychological phenomena. In abroad sense, CPS encompasses learning, decision-making, and acting in complex and dynamic situations. Moreover, solutions to problems that people face in such situations are often generated in teams or groups. This adds another layer of complexity to the situation itself because of the emerging issues that arise from the social dynamics of group |
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interactions. This framing of CPS means that it is not a single construct that can be measured by using a particular type of CPS task (e.g. minimal complex system tests), which is a view taken by the psychometric community. The proposed approach taken here is that because CPS is multifaceted, multiple approaches need to be taken to fully capture and understand what it is and how the different cognitive processes associated with it complement each other.Thus, this Research Topic is aimed at showcasing the latest work in the fields of CPS, as well as DDM and CDC that takes a holist approach to investigating and theorizing about these abilities. The collection of articles encompasses conceptual approaches as well as experimental and correlational studies involving established or new tools to examine CPS, DDM and CDC. This work contributes to answering questions about what strategies and what general knowledge can be transferred from one type of complex and dynamic situation to another, what learning conditions result in transferable knowledge and skills, and how these features can be trained. |
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