Journal articles: 'Data Modeling and Design'

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Simsion, Graeme, Simon K. Milton, and Graeme Shanks. "Data modeling: Description or design?" Information & Management 49, no. 3-4 (May 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, no. 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, and 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, and Y. Liu. "Modeling nonlinearity in dilution design microarray data." Bioinformatics 23, no. 11 (January 19, 2007): 1339–47. http://dx.doi.org/10.1093/bioinformatics/btm002.

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F, Ryan, and Fitrah R. "Applications Data Warehouse for Sales Analysis Based on Multidimensional Modeling (MDM) and Star Schema Design." International Journal of Psychosocial Rehabilitation 24, no. 02 (February 13, 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, no. 1 (June 1, 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, no. 6 (1998): 51–73. http://dx.doi.org/10.1109/79.733496.

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Sun, Sherry X., and J. Leon Zhao. "Formal workflow design analytics using data flow modeling." Decision Support Systems 55, no. 1 (April 2013): 270–83. http://dx.doi.org/10.1016/j.dss.2013.01.028.

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Cho, Byung Rae, and Chanseok Park. "Robust design modeling and optimization with unbalanced data." Computers & Industrial Engineering 48, no. 2 (March 2005): 173–80. http://dx.doi.org/10.1016/j.cie.2005.01.004.

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Fattah, Ahmad Maulana Malik, Taufik Ridwan, and Nina Sulistiyowati. "Dimensional Data Design for Event Feedback Data Warehouse." JISA(Jurnal Informatika dan Sains) 6, no. 1 (June 27, 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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Gan, Tian, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, et al. "CSDMS Data Components: data–model integration tools for Earth surface processes modeling." Geoscientific Model Development 17, no. 5 (March 15, 2024): 2165–85. http://dx.doi.org/10.5194/gmd-17-2165-2024.

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Abstract. Progress in better understanding and modeling Earth surface systems requires an ongoing integration of data and numerical models. Advances are currently hampered by technical barriers that inhibit finding, accessing, and executing modeling software with related datasets. We propose a design framework for Data Components, which are software packages that provide access to particular research datasets or types of data. Because they use a standard interface based on the Basic Model Interface (BMI), Data Components can function as plug-and-play components within modeling frameworks to facilitate seamless data–model integration. To illustrate the design and potential applications of Data Components and their advantages, we present several case studies in Earth surface processes analysis and modeling. The results demonstrate that the Data Component design provides a consistent and efficient way to access heterogeneous datasets from multiple sources and to seamlessly integrate them with various models. This design supports the creation of open data–model integration workflows that can be discovered, accessed, and reproduced through online data sharing platforms, which promotes data reuse and improves research transparency and reproducibility.

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Rybakova, Angelina, and Pavel Kagan. "Application of Building Information Modeling in Data Center design." IOP Conference Series: Materials Science and Engineering 869 (July 10, 2020): 022006. http://dx.doi.org/10.1088/1757-899x/869/2/022006.

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Alavi, M., and J. C. Wetherbe. "Mixing prototyping and data modeling for information-system design." IEEE Software 8, no. 3 (May 1991): 86–91. http://dx.doi.org/10.1109/52.88948.

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Hayne, Stephen, and Sudha Ram. "Group data base design: Addressing the view modeling problem." Journal of Systems and Software 28, no. 2 (February 1995): 97–116. http://dx.doi.org/10.1016/0164-1212(94)00048-r.

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Choi, Jung-In. "K-Grid Integrated Data Modeling and Data Exchange Protocol Design and Evaluation." Journal of the Korean Institute of Illuminating and Electrical Installation Engineers 25, no. 12 (December 30, 2011): 119–31. http://dx.doi.org/10.5207/jieie.2011.25.12.119.

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Siddall, J. N. "Probabilistic Modeling in Design." Journal of Mechanisms, Transmissions, and Automation in Design 108, no. 3 (September 1, 1986): 330–35. http://dx.doi.org/10.1115/1.3258735.

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A general procedure is proposed for evolving the form of a density function that is consistent with the concept of subjective probability. The procedure directly applies new data information to the updating of the form of a density function without imposing on it any theoretical distribution that could restrict its shape, and permits the direct use of judgment arising from real world experience. It is based on the simple concept that sample size is a measure of confidence in the shape of a density function. Two possible algorithms are given, and the concept is extended for simple “true” or “false” events. The importance of probability in artificial intelligence is also dicusssed, and its essentially subjective nature is described. Procedures are briefly suggested.

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Baranova, Olga. "Open data formats in building information modeling." E3S Web of Conferences 263 (2021): 04062. http://dx.doi.org/10.1051/e3sconf/202126304062.

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Building Information Modeling (BIM) technology is one of the most actively developing approaches to the digital representation of the design of buildings and structures, which makes it possible to ensure the relationship of both geometric and functional characteristics of a designed object. The organization of information exchange within the framework of the development of IM during various stages of the life cycle is a rather difficult task, since the historical development of software products used for the design of IM elements has led to the use of various data presentation formats for solving specialized design and calculation problems. In the documents analyzed in the work, two formats with an open specification — IFC and XML — are mentioned as a means of information exchange in the development of IM. In addition to the undoubted advantages of using the IFC using the EXPRESS data specification language as a means of ensuring the interoperability of information systems, there are currently difficulties with the practical application of this format in information modeling, including for organizing joint work. XML-schema can be used as an alternative to the representation of IM in the IFC for organizing data exchange between various information systems, including when implementing joint work on IM through web applications. The use of alternative to EXPRESS schemes for the definition of IM data makes it possible to simplify the organization of information transfer between participants in the information exchange, as well as to unify the presentation of design information.

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Foley, T. A., H. Hagen, and G. M. Nielson. "Visualizing and modeling unstructured data." Visual Computer 9, no. 8 (August 1993): 439–49. http://dx.doi.org/10.1007/bf01888718.

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Kim, Byeong Soo, Bong Gu Kang, Seon Han Choi, and Tag Gon Kim. "Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system." SIMULATION 93, no. 7 (June 9, 2017): 579–94. http://dx.doi.org/10.1177/0037549717692866.

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Recently, big data has received greater attention in diverse research fields, including medicine, science, engineering, management, defense, politics, and others. Such research uses big data to predict target systems, thereby constructing a model of the system in two ways: data modeling and simulation modeling. Data modeling is a method in which a model represents correlation relationships between one set of data and the other set of data. On the other hand, physics-based simulation modeling (or simply simulation modeling) is a more classical, but more powerful, method in which a model represents causal relationships between a set of controlled inputs and corresponding outputs. This paper (i) clarifies the difference between the two modeling approaches, (ii) explains their advantages and limitations and compares each characteristic, and (iii) presents a complementary cooperation modeling approach. Then, we apply the proposed modeling to develop a greenhouse control system in the real world. Finally, we expect that this modeling approach will be an alternative modeling approach in the big data era.

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OHTANI, Kaito, Kentaro YAJI, Shintaro YAMASAKI, and Kikuo FUJITA. "Optimal design of natural convection heat sinks by data-driven multifidelity topology design." Proceedings of OPTIS 2022.14 (2022): U00034. http://dx.doi.org/10.1299/jsmeoptis.2022.14.u00034.

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Li, Pengcheng, Chen Ding, and Hao Luo. "Modeling heap data growth using average liveness." ACM SIGPLAN Notices 49, no. 11 (May 11, 2015): 71–82. http://dx.doi.org/10.1145/2775049.2602997.

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Zacharopoulos, Theologos, Pablo Inostroza, and Tijs van der Storm. "Extensible modeling with managed data in Java." ACM SIGPLAN Notices 52, no. 3 (May 12, 2017): 25–35. http://dx.doi.org/10.1145/3093335.2993240.

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Arner, Stanford L., James A. Westfall, and Charles T. Scott. "Comparison of Annual Inventory Designs Using Forest Inventory and Analysis Data." Forest Science 50, no. 2 (April 1, 2004): 188–203. http://dx.doi.org/10.1093/forestscience/50.2.188.

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Abstract Three annual inventory designs, a periodic design, and a periodic measurement with midcycle update design are compared using a population created from 14,754 remeasured Forest Inventory and Analysis plots. Two of the annual designs and the midcycle update design allow updating of plots not measured using sampling with partial replacement procedures. Comparisons are based on root mean square error and estimator bias determined for net volume (m3/hectare) and mean annual net volume change. For the annual designs, both individual year and moving average estimates are investigated. The latter are compared to both the population means of the most recent year used in the average and to population means covering the same period as the estimate. Among annual designs, a rotating panel design, in which an equal portion of the total sample is measured each year without remeasurement until the start of the next measurement cycle, produced the smallest root mean square error for estimates of mean net volume. For multiple-year comparisons, the rotating panel and periodic designs resulted in the smallest root mean square errors; for single-year comparisons, the periodic design resulted in the smallest root mean square error. For mean annual volume change, the smallest root mean square error was produced by the periodic design. Among annual designs, the rotating panel design resulted in the smallest root mean square error for multiple-year comparisons of volume change, while a design allowing annual updates of estimates using generalized least squares resulted in the smallest root mean square error for single-year comparisons. FOR. SCI. 50(2):188–203.

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Selwyn, Justus. "Design of Knowledgebase for EES using Object - Relational Data Modeling." International Journal of Computer Applications 184, no. 21 (July 19, 2022): 8–14. http://dx.doi.org/10.5120/ijca2022922234.

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Paschke, Fabian, Chenzi Huang, and Stephan Seidel. "MPC Design for an Auditorium Building Using Data Driven Modeling." IFAC-PapersOnLine 55, no. 20 (2022): 103–8. http://dx.doi.org/10.1016/j.ifacol.2022.09.079.

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Li, Shen, Xiao Dong Shao, Zhi Hua Zhang, and Xiao Bo Ge. "Parameter-Level Data Flow Modeling Oriented to Product Design Process." International Journal of Bifurcation and Chaos 25, no. 14 (December 30, 2015): 1540037. http://dx.doi.org/10.1142/s0218127415400374.

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In this paper, a method of data flow modeling for a product design process oriented to data parameter is proposed. The data parameters are defined, which are classified as the basic data parameters and complex data parameters. The mechanism of the mapping relationship between different forms of documents and some basic data parameters, and a data transmission based on parameters, are constructed. Aiming at the characteristics of the iterative design process, the parameters version mechanism including node modification and iteration information is proposed. The data parameters transmission relationships are represented by a parameters network model (PNM) based on a directed graph. Finally, through the table of data parameters mapping onto the workflow node and PNM, the data ports and data links in the data flow model are generated automatically by the program. Validation in the 15-meter-diameter S/Ka frequency band antenna design process of the “Reflector, Back frame and Center part design” data flow model shows that the method can effectively shorten the time of data flow modeling and improve the data transmission efficiency.

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Hardiansyah, Alim, Ravie Kurnia Laday, and Muhammad Suhaeli. "The Design of Population Data Application Using Unified Modeling Language." Jurnal Online Informatika 3, no. 2 (February 1, 2019): 74. http://dx.doi.org/10.15575/join.v3i2.227.

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Kahraman, Nilufer, Paul De Boeck, and Rianne Janssen. "Modeling DIF in Complex Response Data Using Test Design Strategies." International Journal of Testing 9, no. 2 (May 8, 2009): 151–66. http://dx.doi.org/10.1080/15305050902880744.

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Tsumura, Koji, and Seiichi Shin. "Simultaneous Modeling and Data-Distribution-Dependent Robust Control System Design." IFAC Proceedings Volumes 30, no. 11 (July 1997): 143–48. http://dx.doi.org/10.1016/s1474-6670(17)42837-0.

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Kwon, Yongjin, Olufemi A. Omitaomu, and Gi-Nam Wang. "Data mining approaches for modeling complex electronic circuit design activities." Computers & Industrial Engineering 54, no. 2 (March 2008): 229–41. http://dx.doi.org/10.1016/j.cie.2007.07.006.

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Song, Li J., and Vikas Mehrotra. "Data Driven CMP Manufacturing Modeling for Process and Design Optimization." ECS Transactions 34, no. 1 (December 16, 2019): 639–45. http://dx.doi.org/10.1149/1.3567651.

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Şengül, Metin, Sıddık B. Yarman, Christian Volmer, and Matthias Hein. "Design of distributed-element RF filters via reflectance data modeling." AEU - International Journal of Electronics and Communications 62, no. 7 (August 2008): 483–89. http://dx.doi.org/10.1016/j.aeue.2007.05.009.

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Periasamy, S. "FPGA Design & Simulation Modeling of Baseband Data Transmission System." IOSR Journal of Electronics and Communication Engineering 6, no. 6 (2013): 06–10. http://dx.doi.org/10.9790/2834-0660610.

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Kwak, Keun-Chang. "A Design of Incremental Granular Network for Software Data Modeling." Journal of Software Engineering and Applications 03, no. 11 (2010): 1027–31. http://dx.doi.org/10.4236/jsea.2010.311120.

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Fortuna, Carolina, Eli De Poorter, Primož Škraba, and Ingrid Moerman. "Data Driven Wireless Network Design: A Multi-level Modeling Approach." Wireless Personal Communications 88, no. 1 (March 15, 2016): 63–77. http://dx.doi.org/10.1007/s11277-016-3242-8.

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Cai, Jie, Xin Wang, Junfeng Shi, Xingxing Xie, Yu Feng, and Yingjun Wu. "Research on a Data-Driven Modeling Method for Precast Concrete Balcony Components." Buildings 14, no. 1 (December 29, 2023): 96. http://dx.doi.org/10.3390/buildings14010096.

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In this paper, a data-driven modeling method for precast concrete (PC) balcony components was proposed to solve the problems of low informatization and the difficult modeling of components at the design stage. Through the analysis of the characteristics of PC balcony components and the combination of modular design methods, the paper designed a data structure for the components and developed a data-driven modeling tool for PC balcony components that can realize the input of structural design data, automatically generating component models. First, this paper introduced the data-driven modeling concept and the modeling process. Second, the PC balcony components in common prefabricated residential projects were analyzed to identify their characteristics. By using a modular design approach, these components were divided and a module dataset was created based on the split modules. Consequently, a data structure for the prefabricated balcony component model was established, wherein both conventional parameters and adaptive parameters between modules were interrelated. Finally, the function of data-driven modeling was achieved by developing a modular design tool on the Revit platform using the C# programming language. The application conducted on a prefabricated building project demonstrated that the software tool and modeling method in this paper effectively improve the level of informatization and modeling efficiency of PC balcony components. The modular design approach was satisfied with the standardization and diversification requirements of balcony components, thereby offering insights for modeling other complex components.

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Chen, Yan Ge. "The Network Management Data Model’s Design and Implementation in Communication Field." Advanced Materials Research 542-543 (June 2012): 1298–301. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1298.

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Starting from the significance and realization principle of data integration, puts forward a modeling method of network element object. According to the network element modeling principles’ abstract analysis on various network data, build the network element model on various data in network. The data integration network element model realizes combining XML with Java language.

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Briggs, T., B. Gischner, P. Lazo, P. Lazo, A. Royal, and R. Wood. "Product Data Exchange to Support Modeling and Simulation." Journal of Ship Production 21, no. 03 (August 1, 2005): 160–69. http://dx.doi.org/10.5957/jsp.2005.21.3.160.

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Successful and efficient exchange of product model data has been a major challenge in the shipbuilding industry for the past two decades. The Standard for the Exchange of Product Model Data (STEP) has been developed to enable this capability. Four STEP application protocols (APs) to facilitate the exchange of structural and distributed systems models in shipbuilding were completed in 2003 and were adopted by the International Organization for Standardization (ISO) by mid-2004. In August 2003, ISO 10303–216: Ship Moulded Forms (AP216) became the first shipbuilding STEP AP to be published as an international standard. Participants involved in these efforts represent several major US shipyards, the Navy, and their computer-aided design/ engineering (CAD/CAE) vendors. The thrust of shipbuilding data exchange efforts has now shifted from development to implementation. This paper will report on efforts to develop and use translators for this AP to exchange hull form product data in the ship modeling and simulation arena. In addition, process simulation is becoming common in the design of new ships to validate that the design meets the customer's specifications. Current technology requires that the ship be modeled both in the computer-aided design (CAD) environment and then repeated in the simulation workbench. Not only is this effort inefficient, but it is inherently error prone. Through the National Shipbuilding Research Program (NSRP)-sponsored Integrated Shipbuilding Environment (ISE) projects, we have developed tool sets that use AP227: Plant Spatial Configuration to permit the design to flow smoothly from the CAD workbench to the simulation workbench. This paper summarizes the efforts to develop and use a suite of tools that enables US shipyards to become more productive. It details the specific successes in using AP216 and AP227 for modeling and simulation, as well as efforts to exchange design data electronically between CAD systems. The report also outlines efforts that are underway to use other APs to successfully exchange data describing ship electrical; heating, ventilation, and air-conditioning (HVAC); and controls systems.

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Gayrard, Emeline, Cédric Chauvière, Hacène Djellout, and Pierre Bonnet. "MODELING EXPERIMENTAL DATA WITH POLYNOMIALS CHAOS." Probability in the Engineering and Informational Sciences 34, no. 1 (August 14, 2018): 14–26. http://dx.doi.org/10.1017/s026996481800030x.

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Given a raw data sample, the purpose of this paper is to design a numerical procedure to model this sample under the form of polynomial chaos expansion. The coefficients of the polynomial are computed as the solution to a constrained optimization problem. The procedure is first validated on samples coming from a known distribution and it is then applied to raw experimental data of unknown distribution. Numerical experiments show that only five coefficients of the Chaos expansions are required to get an accurate representation of a sample.

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Marinus, Benoit G., and Antoine Hauglustaine. "Data-Driven Modeling of Fuel Consumption for Turboprop-Powered Civil Airliners." Energies 13, no. 7 (April 3, 2020): 1695. http://dx.doi.org/10.3390/en13071695.

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Next to empirical correlations for the specific range, fuel flow rate, and specific fuel consumption, a response surface model for estimates of the fuel consumption in early design stages is presented and validated. The response-surface’s coefficients are themselves predicted from empirical correlations based solely on the operating empty weight. The model and correlations are all derived from fuel consumption data of nine current civil turbo-propeller aircraft and are validated on a separate set. The model can accurately predict fuel weights of new designs for any combination of payload and range within the current range of efficiency of the propulsion. The accuracy of the model makes it suited for preliminary and conceptual design of near-in-kind turbo-propeller aircraft. The model can shorten the design cycle by delivering fast and accurate fuel weight estimates from the first design iteration once the operating empty weight is known. Since it is based solely on the operating empty weight and it is accurate, the model is a sound variant to the Breguet range equation in order to make accurate fuel weight estimates.

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Li, Xiaolong, Jiansi Yang, Bingxuan Guo, Hua Liu, and Jun Hua. "Dynamic modeling of tunnel survey spatiotemporal data." Boletim de Ciências Geodésicas 20, no. 2 (June 2014): 354–75. http://dx.doi.org/10.1590/s1982-21702014000200021.

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Currently, for tunnels, the design centerline and design cross-section with time stamps are used for dynamic three-dimensional (3D) modeling. However, this approach cannot correctly reflect some qualities of tunneling or some special cases, such as landslips. Therefore, a dynamic 3D model of a tunnel based on spatiotemporal data from survey cross-sections is proposed in this paper. This model can not only playback the excavation process but also reflect qualities of a project typically missed. In this paper, a new conceptual model for dynamic 3D modeling of tunneling survey data is introduced. Some specific solutions are proposed using key corresponding technologies for coordinate transformation of cross-sections from linear engineering coordinates to global projection coordinates, data structure of files and database, and dynamic 3D modeling. A 3D tunnel TIN model was proposed using the optimized minimum direction angle algorithm. The last section implements the construction of a survey data collection, acquisition, and dynamic simulation system, which verifies the feasibility and practicality of this modeling method.

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Antani, Tithi Anish. "Design and Simulation of Photovoltaic Array." Asian Journal of Electrical Sciences 8, S1 (June 5, 2019): 65–70. http://dx.doi.org/10.51983/ajes-2019.8.s1.2303.

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This paper illustrates a simple yet accurate method of modelling photovoltaic array. By adjusting the curve at three points: open circuit, maximum power, and short circuit, the parameter of the nonlinear equation can be determined. The information about these three major points is provided by all commercial array datasheets, using this data we can easily find the best I-V equation for the single-diode photovoltaic (PV) model. Adjusting the parameters of I–V equation, one can build a PV circuit model with any circuit simulator by using basic math blocks. The proposed circuit model and modeling method and are useful for power electronics designers who need a simple, fast, accurate, and easy-to-use modeling method for using in simulations of PV systems.

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Camilovic, Dragana, Dragana Becejski-Vujaklija, and Natasa Gospic. "A call detail records data mart: Data modeling and OLAP analysis." Computer Science and Information Systems 6, no. 2 (2009): 87–110. http://dx.doi.org/10.2298/csis0902087c.

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In order to succeed in the market, telecommunications companies are not competing solely on price. They have to expand their services based on their knowledge of customers' needs gained through the use of call detail records (CDR) and customer demographics. All the data should be stored together in the CDR data mart. The paper covers the topic of its design and development in detail and especially focuses on the conceptual/logical/physical trilogy. Some other design problems are also discussed. An important area is the problem involving time. This is why the implication of time in data warehousing is carefully considered. The CDR data mart provides the platform for Online Analytical Processing (OLAP) analysis. As it is presented in this paper, an OLAP system can help the telecommunications company to get better insight into its customers' behavior and improve its marketing campaigns and pricing strategies.

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Singh, Vijay. "Software Professionals use Object Oriented data modeling instead of traditional relational data modeling." International Journal of Advance Research and Innovation 5, no. 3 (2017): 17–25. http://dx.doi.org/10.51976/ijari.531704.

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The purpose of this paper is to explain why object oriented data modeling is more popular than relational data modeling. A data model is a logic organization of the real world objects (entities), constraints on them, and the relationships among objects. Relational model is very simple since data in represented in the form of relations that are depicted by use of two-dimensional tables. Rows in the table represent records and Columns represent attributes of the entity. The basic concept in the relational model is that of a relation. In object-oriented model main construct is an object. As in relational model, there are relations similarly we have objects in OO data modeling. So first thing in OO model is to identify the objects for the systems. Examining the problem statement can do it. Other important task is to identify the various operations for these objects. It is easy to relate the objects to the real world entity. The object-oriented approach has proved to be especially fruitful in application areas, such as the design of geographical information systems which have a richly structured knowledge domain and are associated with multimedia databases. Relational data modeling is different from Object Oriented data modeling because it focuses solely on data while object oriented data models focuses on both the behavior and data aspects of your domain. OODBMS are faster than relational DBMS because data isn’t stored in relational rows and columns but as objects. Objects have a many to many relationship and are accessed by the use of pointers.

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Chen, Binjin, Shaohua Jiang, Ligang Qi, Yawu Su, Yufeng Mao, Meng Wang, and Hee Sung Cha. "Design and Implementation of Quantity Calculation Method Based on BIM Data." Sustainability 14, no. 13 (June 27, 2022): 7797. http://dx.doi.org/10.3390/su14137797.

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Manual quantity takeoff using two-dimensional (2D) drawings and personal knowledge is error-prone and time-consuming. Theoretically, quantity can be automatically calculated from building information model more quickly and reliably by extracting geometric data and semantic attributes of building elements. Specific construction classification systems embedded in mainstream modeling software for building information modeling (BIM) make it difficult for countries adopting different systems to calculate quantity directly. This paper proposes a BIM-based quantity takeoff code mapping (BQTCM) method to solve the above issue, and develops a quantity takeoff code mapping plug-in (QTCMP) on a BIM modeling software based on the proposed BQTCM method to obtain an accurate bill of quantities directly and efficiently. Moreover, by conducting a statistical analysis and examining a case study, this paper verifies the accuracy and efficiency of quantity takeoff attained from the proposed BQTCM method and QTCMP.

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Cheng, Chiyi, Mingmin Zhang, and Zhigeng Pan. "Multi-Resolution Modeling for Virtual Design." International Journal of Virtual Reality 4, no. 4 (January 1, 2000): 110–20. http://dx.doi.org/10.20870/ijvr.2000.4.4.2660.

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The benefits of multi-resolution modeling techniques in virtual reality are vast, but one essential component of this model is how it can be used to speedup the process of virtual design and virtual prototyping. In this paper we propose a new multi-resolution representation scheme called MRM, which can support efficient extraction of both fixed and variable resolution modeling data for handling multiple objects in the same scene. One important feature of the MRM scheme is that it supports unified selective simplifications and selective refinements over the mesh representation of the object. In addition, multi-resolution models may be used to support real-time geometric transmission of data in collaborative virtual design and prototyping applications. These key features in MRM, may be applied to a variety of VR applications.

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Schultz, R. R., and R. L. Stevenson. "Stochastic modeling and estimation of multispectral image data." IEEE Transactions on Image Processing 4, no. 8 (1995): 1109–19. http://dx.doi.org/10.1109/83.403416.

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Wilson, P. R., I. D. Faux, M. C. Ostrowski, and K. G. Pasquill. "Interfaces for Data Transfer Between Solid Modeling Systems." IEEE Computer Graphics and Applications 5, no. 1 (January 1985): 41–51. http://dx.doi.org/10.1109/mcg.1985.276272.

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Sampaio, Alcinia Zita, and Augusto Martins Gomes. "BIM Interoperability Analyses in Structure Design." CivilEng 2, no. 1 (February 14, 2021): 174–92. http://dx.doi.org/10.3390/civileng2010010.

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The building information modelling (BIM) methodology supports collaborative works, based on the centralization of all information in a federated BIM model and on an efficient level of interoperability between BIM-based platforms. Concerning the structure design, the interoperability capacity of the most used software presents limitations that must be identified and alternative solutions must be proposed. This study analyzes the process of transfer of structure models between modeling and structure analysis tools. Distinct building cases were performed in order to recognize the type of limitations verified in the transfer processes concerning two-way data flow between several software. The study involves the modeling software ArchiCAD 2020, Revit 2020, and AECOsim 2019 and the structure analyzes tools SAP 2020, Robot 2020, and ETABS 22020. The transfer processes are realized in two ways: using the native data format; using a universal standard data transfer, the Industry Foundation Classes (IFC) format. The level of maturity of BIM in structure design is still relatively low, caused essentially by interoperability problems, but despite the limitations detected, this study shows throughout the development of several building case, that the methodology has clear advantages in the development of the structure project.

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Qiao, Lihong, Yifan Qie, and Yixin Zhu. "Modeling for assembly simulations: Data description and a data-driven construction framework." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 02 (April 4, 2017): 1750044. http://dx.doi.org/10.1142/s1793962317500441.

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Assembly simulations such as assembly process simulation and assembly tolerance simulation have become an effective means to evaluate and analyze product assembly design and assembly process planning. Being core aspect of simulation implementation, building an assembly simulation model is rather time-consuming because of its high complexity. Furthermore, modeling has a significant influence on the popularization and application of simulation technology. In this paper, data needed by assembly process and tolerance simulation are addressed to propose a data-driven approach for assembly simulation modeling. The application process and the architecture of modeling framework for assembly simulation are presented as well. An assembly sequence simulation example is given to illustrate the application of the framework. The framework provides a new idea for the realization of automatic modeling for assembly simulation.

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Journal articles on the topic 'Data Modeling and Design'

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