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TRANSFORMING DATA INTO ADDED-VALUE INFORMATION: THE DESIGN OF SCIENTIFIC MEASUREMENT MODELS THROUGH THE LENS OF DESIGN THEORY

Published online by Cambridge University Press:  27 July 2021

Raphaëlle Barbier ,
Pascal Le Masson  and
Benoit Weil
Raphaëlle Barbier*
Affiliation:
MINES ParisTech
Pascal Le Masson
Affiliation:
MINES ParisTech
Benoit Weil
Affiliation:
MINES ParisTech
*
Barbier, Raphaelle, MINES ParisTech, Centre for Management Sciences, France, [email protected]

Abstract

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Transforming data into added-value information is a recurrent issue in the context of “big data” phenomenon, as new sources of data become increasingly available. This paper proposes to offer a fresh look on how data and added-value information are linked through the design of specific models. This investigation is based on design theory, used as an analysis framework, and on a historical example in the Earth science field. It aims at unveiling the reasoning logic behind the design process of models combining data science and domain knowledge in specific ways, especially involving not only knowledge about the physical phenomena but also on the measuring instrument itself. More specifically, this paper shows how specific efforts on exploring the originality of the new instrument compared to existing ones can result in designing performant models to transform new sources of data into information. This also suggests several important competencies to be involved in the model-design process: (1) a detailed understanding of the limitations of existing models (2) the ability to explore both the originality of the new source of data compared to existing ones (3) the ability of leveraging independent data sources.

Keywords

Design theoryBig dataDesign processdata scienceinformation design
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 3239 - 3248
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Abbasi, A., Sarker, S., Chiang, R.H.L., 2016. Big Data Research in Information Systems: Toward an Inclusive Research Agenda. J. AIS 17, 3. https://doi.org/10.17705/1jais.00423Google Scholar
Barnes, C.J., 1995. The art of catchment modeling: What is a good model? Environment International, Water Modelling 21, 747–751. https://doi.org/10.1016/0160-4120(95)00082-VGoogle Scholar
Bertoni, A., 2020. DATA-DRIVEN DESIGN IN CONCEPT DEVELOPMENT: SYSTEMATIC REVIEW AND MISSED OPPORTUNITIES. Proceedings of the Design Society: DESIGN Conference 1, 101–110. https://doi.org/10.1017/dsd.2020.4CrossRefGoogle Scholar
Beven, K., 1989. Changing ideas in hydrology — The case of physically-based models. Journal of Hydrology 105, 157–172. https://doi.org/10.1016/0022-1694(89)90101-7CrossRefGoogle Scholar
Dammak, H., Gardoni, M., 2018. Improving the Innovation Process by Harnessing the Usage of Content Management Tools Coupled with Visualization Tools, in: Chiabert, P., Bouras, A., Noël, F., Ríos, J. (Eds.), Product Lifecycle Management to Support Industry 4.0, IFIP Advances in Information and Communication Technology. pp. 642–655. https://doi.org/10.1007/978-3-030-01614-2_59Google Scholar
Escandón-Quintanilla, M.-L., Gardoni, M., Cohendet, P., 2018. Improving concept development with data exploration in the context of an innovation and technological design course. International Journal on Interactive Design and Manufacturing (IJIDeM) 12, 161–172. https://doi.org/10.1007/s12008-017-0380-5CrossRefGoogle Scholar
Gandomi, A., Haider, M., 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management 35, 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007CrossRefGoogle Scholar
Giordani, A., Mari, L., 2012. Measurement, Models, and Uncertainty. IEEE Transactions on Instrumentation and Measurement 61, 2144–2152. https://doi.org/10.1109/TIM.2012.2193695CrossRefGoogle Scholar
Grüter, W., Guillard, H., Möser, W., Monget, J.M., Palz, W., Raschke, E., Reinhardt, R.E., Schwarzmann, P., Wald, L., 1986. Solar Radiation Data from Satellite Images: Determination of Solar Radiation at Ground Level from Images of the Earth Transmitted by Meteorological Satellites - An Assessment Study, Solar Energy R&D in the Ec Series F: Springer Netherlands.Google Scholar
Günther, W.A., Rezazade Mehrizi, M.H., Huysman, M., Feldberg, F., 2017. Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems 26, 191–209. https://doi.org/10.1016/j.jsis.2017.07.003CrossRefGoogle Scholar
Hatchuel, A., Weil, B., 2009. C-K design theory: an advanced formulation. Research in Engineering Design 19, 181–192. https://doi.org/10.1007/s00163-008-0043-4CrossRefGoogle Scholar
Hatchuel, A., Weil, B., 2003. A new approach of innovative design: an introduction to C-K theory. Presented at the International Conference on Engineering Design, International Conference on Engineering Design, Stockholm.Google Scholar
Huron, S., Carpendale, S., Thudt, A., Tang, A., Mauerer, M., 2014. Constructive visualization, in: Proceedings of the 2014 Conference on Designing Interactive Systems, DIS ’14. Association for Computing Machinery, New York, NY, USA, pp. 433–442. https://doi.org/10.1145/2598510.2598566CrossRefGoogle Scholar
Karpatne, A., Atluri, G., Faghmous, J.H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., Kumar, V., 2017. Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data. IEEE Transactions on Knowledge and Data Engineering 29, 2318–2331. https://doi.org/10.1109/TKDE.2017.2720168CrossRefGoogle Scholar
Kazakçı, A.O., 2015. DATA SCIENCE AS A NEW FRONTIER FOR DESIGN. Presented at the Proceedings of the 20th International Conference on Engineering Design (ICED15), p. 10.Google Scholar
Kim, H.H.M., Liu, Y., Wang, C.C.L., Wang, Y., 2017. Special Issue: Data-Driven Design (D3). J. Mech. Des 139. https://doi.org/10.1115/1.4037943CrossRefGoogle Scholar
Mari, L., Carbone, P., Petri, D., 2012. Measurement Fundamentals: A Pragmatic View. IEEE Transactions on Instrumentation and Measurement 61, 2107–2115. https://doi.org/10.1109/TIM.2012.2193693CrossRefGoogle Scholar
Montecchi, T., Becattini, N., 2020. Design for sustainable behavior: opportunities and challenges of a data-driven approach.Proceedings of the Design Society: DESIGN Conference 1, 2089–2098. https://doi.org/10.1017/dsd.2020.147CrossRefGoogle Scholar
Noia, M., Ratto, C.F., Festa, R., 1993a. Solar irradiance estimation from geostationary satellite data: I. Statistical models. Solar Energy 51, 449–456. https://doi.org/10.1016/0038-092X(93)90130-GCrossRefGoogle Scholar
Noia, M., Ratto, C.F., Festa, R., 1993b. Solar irradiance estimation from geostationary satellite data: II. Physical models. Solar Energy 51, 457–465. https://doi.org/10.1016/0038-092X(93)90131-7CrossRefGoogle Scholar
Parraguez, P., Maier, A., 2017. Data-driven engineering design research: Opportunities using open data Proceedings of the 21st International Conference on Engineering Design (ICED 17) Vol 7: Design Theory and Research Methodology, Vancouver, Canada, 21-25.08.2017.Google Scholar
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., Prabhat, 2019. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204. https://doi.org/10.1038/s41586-019-0912-1CrossRefGoogle ScholarPubMed
Rowley, J., 2007. The wisdom hierarchy: representations of the DIKW hierarchy, The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science 33, 163–180. https://doi.org/10.1177/0165551506070706CrossRefGoogle Scholar
Sitruk, Y., Kazakçi, A., 2018. CROWD-BASED DATA-DRIVEN HYPOTHESIS GENERATION FROM DATA AND THE ORGANISATION OF PARTICIPATIVE SCIENTIFIC PROCESS. Presented at the 15th International Design Conference, pp. 1673–1684. https://doi.org/10.21278/idc.2018.0510CrossRefGoogle Scholar
Tal, E., 2017. Calibration: Modelling the measurement process. Studies in History and Philosophy of Science Part A, The Making of Measurement 65-66, 33–45. https://doi.org/10.1016/j.shpsa.2017.09.001Google Scholar
Wald, L., 1998. Data fusion: a conceptual approach for an efficient exploitation of remote sensing images. Presented at the 2nd International Conference “Fusion of Earth Data: merging point measurements, raster maps and remotely sensed images”, Sophia-Antipolis (France), p. 8.Google Scholar
Zins, C., 2007. Conceptual approaches for defining data, information, and knowledge. Journal of the American Society for Information Science and Technology 58, 479–493. https://doi.org/10.1002/asi.20508CrossRefGoogle Scholar