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Статті в журналах з теми "Design for data"

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Автор: Grafiati

Опубліковано: 7 липня 2024

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1

Fatima, Neha, and Prof S. A. Madival. "A Design of Lightweight Secure Data Sharing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1965–70. http://dx.doi.org/10.31142/ijtsrd14520.

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2

Rusdiana, Komang Ery, linawati linawati, and I. Made Oka Widyantara. "Green Data Center Design of Udayana University." Majalah Ilmiah Teknologi Elektro 15, no. 2 (December 15, 2016): 39–44. http://dx.doi.org/10.24843/mite.1502.07.

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3

王, 倩. "Analysis of Wardrobe User Needs Based on Data Statistics." Design 09, no. 03 (2024): 846–53. http://dx.doi.org/10.12677/design.2024.93391.

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4

李, 月恩. "Research on Product Design Methods Based on Image Data Mining." Design 08, no. 04 (2023): 2198–204. http://dx.doi.org/10.12677/design.2023.84265.

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5

张, 鸿莉. "Data-Based Interior Design: Practice and Application of Machine Learning." Design 09, no. 03 (2024): 1015–22. http://dx.doi.org/10.12677/design.2024.93412.

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6

Banks, David L., and Colin L. Mallows. "Design, Data, Analysis." Journal of the American Statistical Association 83, no. 404 (December 1988): 1214. http://dx.doi.org/10.2307/2290162.

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7

Golbeck, Jennifer. "Data meets design." Science 347, no. 6217 (January 1, 2015): 36. http://dx.doi.org/10.1126/science.aaa0421.

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8

Sherwin, David John. "Design from data." New Scientist 204, no. 2732 (October 2009): 28–29. http://dx.doi.org/10.1016/s0262-4079(09)62875-x.

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9

Nethercot, D. A. "Design data sheet." Journal of Constructional Steel Research 7, no. 1 (January 1987): 75–77. http://dx.doi.org/10.1016/0143-974x(87)90035-6.

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10

Clark, R. G. "Sample Design Using Imperfect Design Data." Journal of Survey Statistics and Methodology 1, no. 1 (May 1, 2013): 6–23. http://dx.doi.org/10.1093/jssam/smt002.

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11

李, 怡龙. "Research on Nanjing Xiaoxihu Neighborhood Vitality Strategy Based on Multi-Source Data." Design 09, no. 03 (2024): 356–63. http://dx.doi.org/10.12677/design.2024.93327.

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12

M. Marvin Newhouse for the MPS Coor. "Data entry design and data quality." Controlled Clinical Trials 6, no. 3 (September 1985): 229. http://dx.doi.org/10.1016/0197-2456(85)90036-4.

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13

Suresh, Geetha, SanjeevV Thomas, and Karthik Suresh. "Design and data analysis 1 study design." Annals of Indian Academy of Neurology 15, no. 2 (2012): 76. http://dx.doi.org/10.4103/0972-2327.94987.

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14

Akin, Ömer, and Chengtah Lin. "Design protocol data and novel design decisions." Design Studies 16, no. 2 (April 1995): 211–36. http://dx.doi.org/10.1016/0142-694x(94)00010-b.

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15

Jin, Y., and Y. Ishino. "DAKA: design activity knowledge acquisition through data-mining." International Journal of Production Research 44, no. 14 (July 15, 2006): 2813–37. http://dx.doi.org/10.1080/00207540600654533.

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16

Facklam, Ferdinand, and Felipe Pecegueiro do Amaral Curado. "Data driven parametric design." PARC Pesquisa em Arquitetura e Construção 2, no. 7 (October 31, 2011): 49. http://dx.doi.org/10.20396/parc.v2i7.8634582.

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Анотація:

The focus of this paper is that we want to give a brief introduction about the idea of Parametric Design (PD) and the use of data to inform the design process. The digital fabrication is not covered in detail in this document. In the case study “Live Building” explains a sensory process. The project shows how to collect data, transformed and transported into a shape. Innovation is not only the approach of the draft, but the systematic procedure and the resulting diversity of solutions. The search for the geometric shape and the key to the concept will be answered in detail.

17

Van Ameijde, Jeroen. "Data-driven Urban Design." SPOOL 9, no. 1 (May 27, 2022): 35–48. http://dx.doi.org/10.47982/spool.2022.1.03.

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Анотація:

Nicholas Negroponte and MIT’s Architecture Machine Group speculated in the 1970s about computational processes that were open to participation, incorporating end-user preferences and democratizing urban design. Today’s ‘smart city’ technologies, using the monitoring of people’s movement and activity patterns to offer more effective and responsive services, might seem like contemporary interpretations of Negroponte’s vision, yet many of the collectors of user information are disconnected from urban policy making. This article presents a series of theoretical and procedural experiments conducted through academic research and teaching, developing user-driven generative design processes in the spirit of ‘The Architecture Machine’. It explores how new computational tools for site analysis and monitoring can enable data-driven urban place studies, and how these can be connected to generative strategies for public spaces and environments at various scales. By breaking down these processes into separate components of gathering, analysing, translating and implementing data, and conceptualizing them in relation to urban theory, it is shown how data-driven urban design processes can be conceived as an open-ended toolkit to achieve various types of user-driven outcomes. It is argued that architects and urban designers are uniquely situated to reflect on the benefits and value systems that control data-driven processes, and should deploy these to deliver more resilient, liveable and participatory urban spaces.

18

Jiang, Lei Ge. "Visualization Design of Data." Applied Mechanics and Materials 325-326 (June 2013): 1564–66. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1564.

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Анотація:

With the explosion of information and the diversify of modern culture, the communication of information have changed a lot, especially the visualization design of data .This paper analyzed the status of Data Visualization in order to expound the significance of Data Visualization and the applications trend .

19

Kemp, A. W., and C. L. Mallows. "Design, Data and Analysis." Biometrics 44, no. 3 (September 1988): 914. http://dx.doi.org/10.2307/2531608.

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20

Alho, Kari, Hannu Peltonen, Martti Mäntylä, and Rejio Sulonen. "A design data manager." ACM SIGMOD Record 17, no. 3 (June 1988): 202. http://dx.doi.org/10.1145/971701.50226.

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21

Wallis, Peter. "FROM DATA TO DESIGN." Applied Artificial Intelligence 25, no. 6 (July 2011): 530–48. http://dx.doi.org/10.1080/08839514.2011.587155.

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22

Hodgson, John. "Data-Directed Drug Design." Nature Biotechnology 9, no. 1 (January 1991): 19–21. http://dx.doi.org/10.1038/nbt0191-19.

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23

Cutts, D., J. S. Hoftun, R. T. Zeller, T. Trojak, R. Van Berg, and C. R. Johnson. "D0 Data Acquisition Design." IEEE Transactions on Nuclear Science 32, no. 4 (August 1985): 1473–78. http://dx.doi.org/10.1109/tns.1985.4333637.

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24

Churchill, Elizabeth F. "Data, design, and ethnography." Interactions 25, no. 1 (December 21, 2017): 22–23. http://dx.doi.org/10.1145/3172893.

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25

Wong, Bang. "Design of data figures." Nature Methods 7, no. 9 (September 2010): 665. http://dx.doi.org/10.1038/nmeth0910-665.

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26

Baker, Michael J. "Data Collection – Questionnaire Design." Marketing Review 3, no. 3 (June 1, 2003): 343–70. http://dx.doi.org/10.1362/146934703322383507.

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27

Gupta, Rishi, and Tim Roughgarden. "Data-driven algorithm design." Communications of the ACM 63, no. 6 (May 21, 2020): 87–94. http://dx.doi.org/10.1145/3394625.

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28

Ziegel, Eric. "Design, Data, and Analysis." Technometrics 31, no. 1 (February 1989): 118–19. http://dx.doi.org/10.1080/00401706.1989.10488488.

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29

Lockett, F. J. "Polymer data for design." Materials & Design 14, no. 4 (August 1993): 260. http://dx.doi.org/10.1016/0261-3069(93)90082-7.

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30

Bean, Jonathan, and Daniela Rosner. "Big data, diminished design?" Interactions 21, no. 3 (May 2014): 18–19. http://dx.doi.org/10.1145/2592267.

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31

Orman, Levent. "Functional data model design." Information Systems 11, no. 2 (January 1986): 123–35. http://dx.doi.org/10.1016/0306-4379(86)90002-5.

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32

König, Christine, Helmut Büttner, and Fritz Ebert. "Design Data for Cyclones." Particle & Particle Systems Characterization 8, no. 1-4 (1991): 301–7. http://dx.doi.org/10.1002/ppsc.19910080155.

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33

Becker, Robert, Laura Steffny, Thomas Bleistein, and Dirk Werth. "From Data to Design." atp magazin 66, no. 6-7 (July 1, 2024): 80–87. http://dx.doi.org/10.17560/atp.v66i6-7.2738.

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Анотація:

This paper explores the application of Large Language Models (LLMs) in the automotive and supplier industries, with a particular focus on the use of retrieval-augmented generation (RAG) systems to streamline information retrieval from technical documentation. The research, part of the CoLab4DigiTwin project, investigates how digital twins supported by smart services can enhance interdisciplinary collaboration and reduce the reliance on manual data searches. We developed a pipeline utilizing a RAG architecture which uses a vector database for efficient data management and fast access to relevant information, eliminating the need for expensive computational resources. The performance of various open-source LLMs, which are finetuned on German, was evaluated, focusing on readability, clarity, and accuracy. The results show decent performance of the system without the need for model fine-tuning. Future research will aim to refine these processes and extend the applicability of RAG systems, highlighting the potential of Large Language Models to transform industrial data interaction.

34

Laznicka, Peter. "Design of an international data base of giant metal accumulations." Global Tectonics and Metallogeny 6, no. 3-4 (January 1, 1998): 215–24. http://dx.doi.org/10.1127/gtm/6/1998/215.

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35

Churchill, Elizabeth F. "From data divination to data-aware design." Interactions 19, no. 5 (September 2012): 10–13. http://dx.doi.org/10.1145/2334184.2334188.

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36

Atri, Preyaa. "Design and Implementation of High-Throughput Data Streams using Apache Kafka for Real-Time Data Pipelines." International Journal of Science and Research (IJSR) 7, no. 11 (November 5, 2018): 1988–91. http://dx.doi.org/10.21275/sr24422184316.

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37

Kumar, Ch Kishore. "Analysis and Design of Data Mining Techniques to Develop Software Reliability." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (March 31, 2020): 167–73. http://dx.doi.org/10.5373/jardcs/v12i4/20201429.

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38

Lee, Shin Haeng. "Combining Design- and Model-based Analyses for Cross-national Survey Data." Korean Data Analysis Society 24, no. 3 (June 30, 2022): 933–42. http://dx.doi.org/10.37727/jkdas.2022.24.3.933.

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Анотація:

Survey weighting matters when different sampling probabilities between countries, as well as within a country, are related to the response variable conditional on its predictors. This study aims at improving estimation of regression coefficients from a cross-national data set by assessing the relative effectiveness of different techniques for survey weighting to gain in precision. Data came from the Asian subset of the World Values Survey. Using pooled data across five Asian countries, the logistic regression analysis is fitted to examine the relationship between information seeking behaviors as predictor variables and political participation as a response variable in the context of East and Southeast Asia. The results show that, sampling weights have an advantage of reducing standard errors when estimating a structural relationship among the variables, so much so that the population is stratified into different countries where the regression model is likely to vary in explaining the within-country data. Therefore, the unequal sampling probability of the cross-national survey needs to be adjusted for by accounting for different probabilities of selection between countries. Incorporating the between-country survey weighting into a model-based analysis is a remedy for the problem in the estimated regression parameters.

39

Raj Kondogi Shiridi, Sai. "Technical Integration Design for Seamless Data Exchange between Taleo and Sterling." International Journal of Science and Research (IJSR) 11, no. 2 (February 5, 2022): 1313–15. http://dx.doi.org/10.21275/sr231019232953.

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40

Kumar, Sankalp, and Dr Febin Prakash. "Secure by Design: Illuminating the Path with Data Science in Cybersecurity." International Journal of Research Publication and Reviews 5, no. 3 (March 9, 2024): 1546–52. http://dx.doi.org/10.55248/gengpi.5.0324.0705.

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41

Shijitha, R., P. Karthigaikumar, and A. Stanly Paul. "Data Warehouse Design for Big Data in Academia." Computers, Materials & Continua 71, no. 1 (2022): 979–92. http://dx.doi.org/10.32604/cmc.2022.016676.

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42

Dash, Ranjan Kumar. "Design of data scoring model for big data." International Journal of Intelligent Enterprise 7, no. 1/3 (2020): 356. http://dx.doi.org/10.1504/ijie.2020.10026356.

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43

Dash, Ranjan Kumar. "Design of data scoring model for big data." International Journal of Intelligent Enterprise 7, no. 1/2/3 (2020): 356. http://dx.doi.org/10.1504/ijie.2020.104666.

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44

Green, David C. "Utility Data Web Page Design– Presenting the Data." Strategic Planning for Energy and the Environment 24, no. 2 (October 2004): 28–42. http://dx.doi.org/10.1080/10485230409509659.

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45

Green, David C., and Paul J. Allen. "Utility Data Web Page Design–Mining the Data." Strategic Planning for Energy and the Environment 25, no. 2 (April 2005): 46–59. http://dx.doi.org/10.1080/10485230509509686.

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46

Nievergelt, J. "Data analysis-the key to data base design." Computer Compacts 4, no. 5 (September 1986): 173. http://dx.doi.org/10.1016/0167-7136(86)90011-9.

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47

Kansha, Yasuki, Yoshihiro Hashimoto, and Min-Sen Chiu. "Data-Based LQI Controller Design from Plant Data." JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 39, no. 7 (2006): 746–52. http://dx.doi.org/10.1252/jcej.39.746.

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48

NORVAISAS, JULIE MARIE, and JONATHAN “YONI” KARPFEN. "Little Data, Big Data and Design at LinkedIn." Ethnographic Praxis in Industry Conference Proceedings 2014, no. 1 (October 2014): 227–36. http://dx.doi.org/10.1111/1559-8918.01029.

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49

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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Анотація:

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.

50

Sembiring, Falentino, and Dian Permata Sari. "Design process data storage and organize data scraping." INTEGRATED (Journal of Information Technology and Vocational Education) 1, no. 1 (April 1, 2019): 17–20. http://dx.doi.org/10.17509/integrated.v1i1.16837.

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Анотація:

In this study Web scraping will explain the process of retrieving urls from similar sites for the erosion process and storing url data on daily, weekly, monthly, and annual databases, so that url data can be valid and invalid urls will be filtered. filtering will be done to make it easier for a number of processes to be moved into the database. The next process will distinguish url based on available content data based on title, tags, keywords like SEO. Each step will be stored in the data warehouse to create the url data center. Hopefully this is the stage to collect data for big data. Problems are limited by designing web crawlers by searching for similar sites and storing processes in the database. From the database it will be directed to the data warehouse data. after in the data warehouse, data will be processed in the interface to the user divided by classification