Thematische Bibliographien / Data Modeling and Design

Auswahl der wissenschaftlichen Literatur zum Thema „Data Modeling and Design“

Autor: Grafiati
Veröffentlicht am 25. Juli 2024
Zuletzt aktualisiert am 31. Juli 2024

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Zeitschriftenartikel zum Thema "Data Modeling and Design"

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Simsion, Graeme, Simon K. Milton und Graeme Shanks. „Data modeling: Description or design?“ Information & Management 49, Nr. 3-4 (Mai 2012): 151–63. http://dx.doi.org/10.1016/j.im.2012.01.003.

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Kumra, Sona. „Data Modeling Techniques for Data Warehouse“. International Journal of Advance Research and Innovation 5, Nr. 2 (2017): 51–53. http://dx.doi.org/10.51976/ijari.521709.

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The conceptual Entity-Relationship (ER) diagrams are extensively used for database design in relational database environment, which emphasized on day-to-day operations. Multidimensional (MD) data modelling, on the other hand, is crucial in data warehouse design, which targeted for managerial decision support. It supports decision making by allowing users to drill-down for a more detailed information, roll-up to view summarized information, slice and dice a dimension for a selection of a specific item of interest and pivot to re-orientate the view of MD data. When designing a MD model regardless whether it is a star or snowflake schema, it involves the identification of a fact, dimensions and measure attributes. This paper will explore on how the Multidimensional model can be used as the solution of data warehouse design instead of ER Model.
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Carvalho, Goncalo, Sergii Mykolyshyn, Bruno Cabral, Jorge Bernardino und Vasco Pereira. „Comparative Analysis of Data Modeling Design Tools“. IEEE Access 10 (2022): 3351–65. http://dx.doi.org/10.1109/access.2021.3139071.

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Zheng, X., H. C. Huang, W. Li, P. Liu, Q. Z. Li und Y. Liu. „Modeling nonlinearity in dilution design microarray data“. Bioinformatics 23, Nr. 11 (19.01.2007): 1339–47. http://dx.doi.org/10.1093/bioinformatics/btm002.

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F, Ryan, und Fitrah R. „Applications Data Warehouse for Sales Analysis Based on Multidimensional Modeling (MDM) and Star Schema Design“. International Journal of Psychosocial Rehabilitation 24, Nr. 02 (13.02.2020): 3927–34. http://dx.doi.org/10.37200/ijpr/v24i2/pr200711.

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Razoqi, Shaymaa. „Data Modeling and Design Implementation for CouchDB Database“. AL-Rafidain Journal of Computer Sciences and Mathematics 15, Nr. 1 (01.06.2021): 39–55. http://dx.doi.org/10.33899/csmj.2021.168252.

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Effros, M. „Optimal modeling for complex system design [data compression]“. IEEE Signal Processing Magazine 15, Nr. 6 (1998): 51–73. http://dx.doi.org/10.1109/79.733496.

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Sun, Sherry X., und J. Leon Zhao. „Formal workflow design analytics using data flow modeling“. Decision Support Systems 55, Nr. 1 (April 2013): 270–83. http://dx.doi.org/10.1016/j.dss.2013.01.028.

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Cho, Byung Rae, und Chanseok Park. „Robust design modeling and optimization with unbalanced data“. Computers & Industrial Engineering 48, Nr. 2 (März 2005): 173–80. http://dx.doi.org/10.1016/j.cie.2005.01.004.

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Fattah, Ahmad Maulana Malik, Taufik Ridwan und Nina Sulistiyowati. „Dimensional Data Design for Event Feedback Data Warehouse“. JISA(Jurnal Informatika dan Sains) 6, Nr. 1 (27.06.2023): 69–73. http://dx.doi.org/10.31326/jisa.v6i1.1648.

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Data is an important asset and a fundamental requirement for building valuable information for organizations. Association of Information Systems Students of Unsika (Himsika) as a university organization provides many events to develop student’s academic and professional skills. A post-event evaluation through a feedback survey was conducted and stored in Google Sheets spreadsheet format. However, the current analysis process using spreadsheets lacks standardization, making it difficult to compare satisfaction rates over time and between events. Additionally, the lack of standardization leads to semi-structured data on spreadsheets, with varying question formats and meanings. To address these limitations, implementing a centralized data warehouse is proposed as a solution. The data warehouse would provide a structured and standardized approach to analyzing event feedback, enabling better comparisons and evaluation of management quality within Himsika. The research aims to design a data warehouse that supports multidimensional analysis. As a way to simplify and optimize analytical queries, the data structure is standardized in the data warehouse. The Four-step Dimensional Design method is applied in designing dimensional modeling on the data warehouse, consisting of four stages including selecting the business process, declaring the grain, identifying the dimensions, and identifying the facts. The design process resulted in 4 dimensions of events, dim_instances, dim_degree_programs, and dim_professions, and a fact table called fact_rates_by_responses. Overall, the proposed data warehouse and dimensional modeling approach aim to enhance the analysis and evaluation of Himsika’s events.
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Dissertationen zum Thema "Data Modeling and Design"

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Chang, Johnny T. 1975. „Data management in a distributed design modeling environment“. Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/88878.

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Jeng, Taysehng. „Design coordination modeling: a distributed computer environment for managing design activities“. Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/23162.

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Zeigler, Patrick Scott. „A solid modeling program for designers“. Thesis, Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/74531.

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The personal computer is rapidly finding its way into the architectural working environment, at this time however it is used mostly as a drafting tool. This thesis is an investigation into programming that would allow the computer to become a design tool. The issues that are dealt with in this thesis include an easy to use user interface that will not inhibit the design process, and develop a system that will allow design changes and additions to a model in a three dimensional sketch like ability. Three dimensional models of paper, clay, wood and other materials have been used to create designs, and aid the designer in making decisions. This type of medium is difficult to make changes, and because of the scale of such materials it becomes difficult to work on interior spaces, thus more attention is usually placed on the exterior design. With the use of the computer these limitations may be eliminated, and the designer may create a design from any perspective or view point.
Master of Architecture
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CHOOBINEH, JOOBIN. „FORM DRIVEN CONCEPTUAL DATA MODELING (DATABASE DESIGN, EXPERT SYSTEMS, CONCEPTUAL)“. Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/188043.

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Conceptual data schema is constructed from the analysis of the business forms which are used in an enterprise. In order to peform the analysis a data model, a forms model, and heuristics to map from the forms model to the data model are developed. The data model we use is an extended version of the Entity-Relationship Model. Extensions include the addition of the min-max cardinalities and generalization hierarchy. By extending the min-max cardinalities to attributes we capture a number of significant characteristics of the entities in a concise manner. We introduce a hierarchical model of forms. The model specifies various properties of each form field within the form such as their origin, hierarchical structure, and cardinalities. The inter-connection of the forms is expressed by specifying which form fields flow from one form to another. The Expert Database Design System creates a conceptual schema by incrementally integrating related collections of forms. The rules of the expert system are divided into six groups: (1) Form Selection, (2) Entity Identification, (3) Attribute Attachment, (4) Relationship Identification, (5) Cardinality Identification, and (6) Integrity Constraints. The rules of the first group use knowledge about the form flow to determine the order in which forms are analyzed. The rules in other groups are used in conjunction with a designer dialogue to identify entities, relationships, and attributes of a schema that represents the collection of forms.
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Phillips, Shawn M. „Improving Marine Corps Total Life Cycle Management by connecting collected data and simulation“. Thesis, Monterey, Calif. : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Jun/09Jun%5FPhillips.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2009.
Thesis Advisor(s): Lucas, Thomas W. "June 2009." Description based on title screen as viewed on July 13, 2009. Author(s) subject terms: Simulation, Design of Experiments, Life Cycle Management, VBA, Modeling. Includes bibliographical references (p. 57). Also available in print.
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Srinivasan, K. „Design and development of an enterprise modeling framework“. Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/8285.

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Snyder, Scott Alan. „Design and Modeling of a Three-Dimensional Workspace“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=case1112875843.

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Swischuk, Renee C. (Renee Copland). „Physics-based machine learning and data-driven reduced-order modeling“. Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122682.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 123-128).
This thesis considers the task of learning efficient low-dimensional models for dynamical systems. To be effective in an engineering setting, these models must be predictive -- that is, they must yield reliable predictions for conditions outside the data used to train them. These models must also be able to make predictions that enforce physical constraints. Achieving these tasks is particularly challenging for the case of systems governed by partial differential equations, where generating data (either from high-fidelity simulations or from physical experiments) is expensive. We address this challenge by developing learning approaches that embed physical constraints. We propose two physics-based approaches for generating low-dimensional predictive models. The first leverages the proper orthogonal decomposition (POD) to represent high-dimensional simulation data with a low-dimensional physics-based parameterization in combination with machine learning methods to construct a map from model inputs to POD coefficients. A comparison of four machine learning methods is provided through an application of predicting flow around an airfoil. This framework also provides a way to enforce a number of linear constraints by modifying the data with a particular solution. The results help to highlight the importance of including physics knowledge when learning from small amounts of data. We also apply a data-driven approach to learning the operators of low-dimensional models. This method provides an avenue for constructing low-dimensional models of systems where the operators of discretized governing equations are unknown or too complex, while also having the ability to enforce physical constraints. The methodology is applied to a two-dimensional combustion problem, where discretized model operators are unavailable. The results show that the method is able to accurately make predictions and enforce important physical constraints.
by Renee C. Swischuk.
S.M.
S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program
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Totapally, Hara. „Virtual design office: A collaborative unified modeling language tool“. CSUSB ScholarWorks, 2001. https://scholarworks.lib.csusb.edu/etd-project/1994.

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Real-time conferencing and collaborative computing is a great way to make developers more effective. This project is a collaborative framework development comprising configurable client and server components.
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Verstak, Alexandre. „Data and Computation Modeling for Scientific Problem Solving Environments“. Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/35299.

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This thesis investigates several issues in data and computation modeling for scientific problem solving environments (PSEs). A PSE is viewed as a software system that provides (i) a library of simulation components, (ii) experiment management, (iii) reasoning about simulations and data, and (iv) problem solving abstractions. Three specific ideas, in functionalities (ii)-(iv), form the contributions of this thesis. These include the EMDAG system for experiment management, the BSML markup language for data interchange, and the use of data mining for conducting non-trivial parameter studies. This work emphasizes data modeling and management, two important aspects that have been largely neglected in modern PSE research. All studies are performed in the context of S4W, a sophisticated PSE for wireless system design.
Master of Science
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Bücher zum Thema "Data Modeling and Design"

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Simsion, Graeme C. Data modeling essentials. 3. Aufl. Amsterdam: Morgan Kaufmann Publishers, 2005.

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Planche, Remi. Data driven systems modeling. Hemel Hempstead: Prentice-Hall, 1991.

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Carbone, Paolo, Sayfe Kiaei und Fang Xu, Hrsg. Design, Modeling and Testing of Data Converters. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39655-7.

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Simsion, Graeme C. Data modeling essentials: Analysis, design, and innovation. New York: Van Nostrand Reinhold, 1994.

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Bobak, Angelo R. Data modeling and design for today's architectures. Boston: Artech House, 1997.

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Simsion, Graeme C. Data modeling essentials: Analysis, design, and innovation. New York: Van Nostrand Reinhold, 1994.

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Sam, Lightstone, und Nadeau Tom, Hrsg. Database modeling & design: Logical design. 4. Aufl. Amsterdam: Elsevier, 2006.

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Mills, G. M. Data modelling and data design. Leicester: The Polytechnic, 1987.

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Jean-Michel, Bergé, Levia Oz und Rouillard Jacques, Hrsg. Modeling in analog design. Boston: Kluwer Academic Publishers, 1995.

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Sarfraz, Muhammad. Advances in geometric modeling. Chichester, West Sussex: Wiley, 2003.

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Buchteile zum Thema "Data Modeling and Design"

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van der Spuy, Rex. „Modeling Game Data“. In AdvancED Game Design with Flash, 1–66. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-2740-3_1.

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Davidson, Louis. „Data Modeling“. In Pro SQL Server 2000 Database Design: Building Quality OLTP Databases, 91–128. Berkeley, CA: Apress, 2004. http://dx.doi.org/10.1007/978-1-4302-0708-5_5.

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van Gils, Bas. „Data Modeling and Design“. In The Enterprise Engineering Series, 87–95. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35539-4_7.

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Tillmann, George. „Logical Data Modeling Definitions“. In Usage-Driven Database Design, 349–51. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2722-0_18.

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Sakka, Amir, Sandro Bimonte, Lucile Sautot, Guy Camilleri, Pascale Zaraté und Aurelien Besnard. „A Volunteer Design Methodology of Data Warehouses“. In Conceptual Modeling, 286–300. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00847-5_21.

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Langer, Arthur M. „Logic Data Modeling Tools“. In Analysis and Design of Information Systems, 80–110. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3492-8_5.

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Theodoratos, Dimitri, und Timos Sellis. „Data Warehouse Schema and Instance Design“. In Conceptual Modeling – ER ’98, 363–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-540-49524-6_29.

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McElreath, Jack. „Data Warehouse Design: Issues in Dimensional Data Modeling“. In Data Management, 173–86. 3. Aufl. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9780429114878-17.

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Ebner, Verena, Boris Otto und Hubert Österle. „Conceptualizing Data in Multinational Enterprises: Model Design and Application“. In Conceptual Modeling, 531–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34002-4_42.

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Simon, Gyorgy, und Constantin Aliferis. „Data Design in Biomedical AI/ML“. In Health Informatics, 341–75. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-39355-6_7.

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AbstractData Design refers to the systematic choice of what data are modeled for analysis and how these data and the model output(s) are mapped between the Problem Space (real-world) and the Model Space (features for the ML modeling). ML data design is an essential element of ML modeling. ML data design differs from classical statistical, epidemiological etc. study designs in that (a) ML data design relies heavily on the existence of digital data repositories that are created independently of the problem solving intent at hand, (b) ML modeling is highly scalable and mostly automated, (c) when using experimental data, ML data design may be used to guide the experiments conducted, (d) uses a richer set of data representations that transcend the classical design matrices such as text, relational databases, graphs etc.; and (e) ML modeling has its own distinct capabilities, limitations and other properties and these are reflected in the data design choices. The present chapter covers tried and tested strategies and protocols that contribute to successful data designs and addresses a number of important biases that threaten validity and generalizability of results. Lower level data transformations, data storage, and security aspects are covered in the “Data Preparation, Transforms, Quality, and Management” chapter.
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Konferenzberichte zum Thema "Data Modeling and Design"

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Mahapatra, Saurabh, und Priyanka Gotika. „Design Data Management in Model-Based Design“. In AIAA Modeling and Simulation Technologies Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-1811.

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Monedero, Javier, und Francisco Muñoz. „Data Organization in City Modeling“. In eCAADe 2003: Digital Design. eCAADe, 2003. http://dx.doi.org/10.52842/conf.ecaade.2003.229.

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Pahle, R., M. Juyal und Filiz Ozel. „DATA MODELING OF BUILDINGS WITH BMXML“. In eCAADe 2003: Digital Design. eCAADe, 2003. http://dx.doi.org/10.52842/conf.ecaade.2003.533.

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Pahle, R., M. Juyal und Filiz Ozel. „DATA MODELING OF BUILDINGS WITH BMXML“. In eCAADe 2003: Digital Design. eCAADe, 2003. http://dx.doi.org/10.52842/conf.ecaade.2003.533.

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Guwaeder, Abdulmunim, Salah Eltief, Ibrahim Aldaouab und Ali Madi. „Modeling monthly insulation data“. In Nonimaging Optics: Efficient Design for Illumination and Solar Concentration XVII, herausgegeben von Roland Winston und Eli Yablonovitch. SPIE, 2020. http://dx.doi.org/10.1117/12.2571736.

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Chuck Monahan. „Symbolic Modeling and Evaluation of Data Paths“. In 32nd Design Automation Conference. ACM, 1995. http://dx.doi.org/10.1109/dac.1995.249979.

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Pernici, Barbara, Marijn Janssen, Giovanni simonini, Giovanni simonini, Giovanni simonini, Giovanni simonini, Giovanni simonini und Giovanni simonini. „Processes and Data“. In Fifth International Symposium on Business Modeling and Software Design. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005884800030003.

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Chen, Jiaying, Nengcheng Chen, Wei Wang, ChuLi Hu und Zhong Zheng. „Remote satellite sensor modeling design and implementation based on the sensor modeling language“. In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, herausgegeben von Yaolin Liu und Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838184.

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Wang, Jian Xun, und Ming Xi Tang. „Product Data Modeling for Design Collaboration“. In 2007 11th International Conference on Computer Supported Cooperative Work in Design. IEEE, 2007. http://dx.doi.org/10.1109/cscwd.2007.4281455.

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Ashok, V., R. Costello und P. Sadayappan. „Modeling Switch-Level Simulation Using Data Flow“. In 22nd ACM/IEEE Design Automation Conference. IEEE, 1985. http://dx.doi.org/10.1109/dac.1985.1586010.

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Berichte der Organisationen zum Thema "Data Modeling and Design"

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Cobb, Maria. Object-Oriented Distributed Database Design and Spatial Data Modeling. Fort Belvoir, VA: Defense Technical Information Center, Januar 2002. http://dx.doi.org/10.21236/ada407824.

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Chamberlain, C. A., und K. Lochhead. Data modeling as applied to surveying and mapping data. Natural Resources Canada/CMSS/Information Management, 1988. http://dx.doi.org/10.4095/331263.

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The Geodetic Survey Division of the Canada Centre for Surveying is replacing the National Geodetic Data Base (NGDB) with the National Geodetic Information System (NGIS). For the NGIS to be successful, it was recognized that a sound, well engineered data mode was essential. The methodology chosen to design the data mode! was Nijssen's Information Analysis Methodology (NIAM), a binary modeling technique that is supported by a Computer Aided Software Engineering (CASE) tool, PC-IAST. An NGIS prototype has also been developed using Digital Equipment of Canada's Relational Database (Rdb) management system and COGNOS Corporations POWERHOUSE 4th generation language. This paper addresses the need for, and the advantages of using a strong engineering approach to data modeling and describes the use of the NIAM methodology in NGIS development. The paper identifies the relationship between the data mode!, data structures, the design and development of a database and the use of automated tools for systems development. In conclusion, critical success factors for the continuation of the N.G.I.S. developments are identified and the benefits that will accrue are enumerated.
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Wilkin, John, Julia C. Levin und Hernan G. Arango. Coastal Ocean Observing System Modeling: Data Assimilation and Adaptive Sampling Design. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada573399.

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Lanning, Christopher, James Dooley, Hojae Yi, Matthew Wamsley und Tristan Dooley. Improved biomass feedstock materials handling and feeding engineering data sets, design methods, and modeling/simulation tools. Office of Scientific and Technical Information (OSTI), April 2023. http://dx.doi.org/10.2172/2222483.

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Ferrer, Emilio. Applied Longitudinal Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/xomktodmxmv62469.

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This comprehensive 3-day workshop is designed to equip researchers with the skills to effectively analyze longitudinal data with classic and advanced modeling techniques. The seminar covers a range of topics from understanding the importance of longitudinal data, handling missing data, modeling growth over time, to practical sessions on applying learned techniques using real-world data, with an official Instats certificate of completion provided at the conclusion. An official Instats certificate of completion and 2 ECTS Equivalent points are provided at the conclusion of the seminar.
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Turgeon, Mathieu. Causal Modeling with Regression Discontinuity Designs (RDD). Instats Inc., 2023. http://dx.doi.org/10.61700/s3nl5lfnmruqw469.

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This seminar introduces the use of the regression discontinuity design (RDD) to estimate treatment effects from observational data. Day 1 topics include directed acyclic graphs (DAG), potential outcomes framework and associated assumptions, the sharp regression discontinuity design, and tools for visualizing discontinuities. Day 2 topics focus on the estimation of values of interest from a regression discontinuity design, adopting a continuity-based approach to RD analysis. Day 2 will also cover issues related to the validation and falsification of the regression discontinuity design. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.
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7

Turgeon, Mathieu. Causal Modeling with Regression Discontinuity Designs (RDD). Instats Inc., 2023. http://dx.doi.org/10.61700/o6e22r1sh4h7m469.

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This seminar introduces the use of the regression discontinuity design (RDD) to estimate treatment effects from observational data. Sessions 1-3 topics include directed acyclic graphs (DAG), potential outcomes framework and associated assumptions, the sharp regression discontinuity design, and tools for visualizing discontinuities. Sessions 4-6 topics focus on the estimation of values of interest from a regression discontinuity design, adopting a continuity-based approach to RD analysis. Day 2 will also cover issues related to the validation and falsification of the regression discontinuity design. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.
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8

Decleir, Cyril, Mohand-Saïd Hacid und Jacques Kouloumdjian. A Database Approach for Modeling and Querying Video Data. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.90.

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Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].
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9

Weijters, Bert. Survey Design and Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/hl5ql4rwahbm1469.

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This seminar will teach you the most current best practices for survey design and analysis. This involves conceptually and statistically modeling, through hands-on exercises, measurement as (a) the selection of indicators of latent constructs, (b) data collection as a psychological process of survey responding, and then (c) specifying and testing measurement models that are able to adequately reflect this process (using CFA). Day 1 offers a conceptual introduction and then a variety of hands-on applications are covered in Mplus on Day 2, and lavaan in R on Day 3. An official Instats certificate of completion is provided at the conclusion of the seminar. The seminar offers 2 ECTS Equivalent points for European PhD students.
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10

Thomson, S. MINE DESIGNER - an automated graphical data generation program integrating numerical modelling with the mine design process. Natural Resources Canada/CMSS/Information Management, 1993. http://dx.doi.org/10.4095/328895.

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