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An exploration-based approach to computationally supported design-by-analogy using D3

Published online by Cambridge University Press:  28 May 2020

Hyeonik Song ,
Jacob Evans  and
Katherine Fu Open the ORCID record for Katherine Fu [Opens in a new window]
Hyeonik Song
Affiliation:
Georgia Institute of Technology, G.W. Woodruff School of Mechanical Engineering, 801 Ferst Drive, MRDC 4508, Atlanta, GA30332-0405, USA
Jacob Evans
Affiliation:
Georgia Institute of Technology, G.W. Woodruff School of Mechanical Engineering, 801 Ferst Drive, MRDC 4508, Atlanta, GA30332-0405, USA
Katherine Fu*
Affiliation:
Georgia Institute of Technology, G.W. Woodruff School of Mechanical Engineering, 801 Ferst Drive, MRDC 4508, Atlanta, GA30332-0405, USA
*
Author for correspondence: Katherine Fu, E-mail: katherine.fu@me.gatech.edu
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Abstract

Computational support for design-by-analogy (DbA) is a growing field, as it aids the process for designers looking to draw inspiration from external sources by harnessing the power of data mining and data visualization. This study presents a unique exploration-based approach for the analogical retrieval process using a computational tool called VISION (Visual Interaction tool for Seeking Inspiration based On Nonnegative Matrix Factorization). Leveraging the U.S. patent database as a source of inspiration, VISION enables designers to visualize a patent repository and explore for analogical inspiration in a user-driven manner. To achieve this, we perform hierarchical Nonnegative Matrix Factorization to generate a clustered structure of patent data and employ D3.js to visualize the patent structure in a node-link network, in which user interaction capabilities are enabled for data exploration. In this study, we also analyze the effect of data size (ranging from 100 to 3000 patents) on two performance aspects of VISION – the clustering quality of topic modeling results and the frame rate of interactive data visualization. The findings show that the tool exhibits more randomized and inconsistent topic modeling results when the database size is too small. But, increasing the database size lowers the frame rate to the point that it could diminish designers’ ability to retrieve and recall information. The scope of the work here is to present the creation of the DbA visualization tool called VISION and to evaluate its data scale limitations in order to provide a basis for developing a visual interaction tool for the analogical retrieval process during DbA.

Keywords

Computational design supportdata miningdata visualizationdesign-by-analogypatents
Type
Research Article
Information
AI EDAM , Volume 34 , Issue 4 , November 2020 , pp. 444 - 457
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Abbott, A and Ellison, M (2008) Biologically inspired textiles. Biologically Inspired Textiles, 1–219. doi:10.1533/9781845695088.Google Scholar
Ahmed, S, Wallace, K and Blessing, L (2003) Understanding the differences between how novice and experienced designers approach design tasks. Research in Engineering Design 14, 1–11. doi:10.1007/s00163-002-0023-z.CrossRefGoogle Scholar
Akin, Ö (1990) Necessary conditions for design expertise and creativity. Design Studies 11, 107–113. doi:10.1016/0142-694X(90)90025-8.CrossRefGoogle Scholar
Atilola, O, Tomko, M and Linsey, JS (2016) The effects of representation on idea generation and design fixation: a study comparing sketches and function trees. Design Studies 42, 110–136. doi:10.1016/j.destud.2015.10.005.CrossRefGoogle Scholar
Basu, T and Murthy, CA (2015) A similarity assessment technique for effective grouping of documents. Information Sciences 311, 149–162. doi:10.1016/j.ins.2015.03.038.CrossRefGoogle Scholar
Bederson, B and Boltman, A (1998) Does Animation Help Users Build Mental Maps of Spatial Information? College Park, MD: Human-Computer Interaction Laboratory, Institute for Advanced Computer Studies.Google Scholar
Bjorklund, TA (2013) Initial mental representations of design problems: differences between experts and novices. Design Studies 34, 135–160. doi:10.1016/j.destud.2012.08.005.CrossRefGoogle Scholar
Bostock, M and Heer, J (2009) Provis: a graphical toolkit for visualization. IEEE Transactions on Visualization and Computer Graphics 15, 1121–1128. doi: 10.1109/TVCG.2009.174CrossRefGoogle Scholar
Bostock, M, Ogievetsky, V and Heer, J (2011) D(3): data-driven documents. IEEE Transactions on Visualization and Computer Graphics 17, 2301–2309. doi:10.1109/TVCG.2011.185.CrossRefGoogle ScholarPubMed
Chakrabarti, A, Sarkar, P, Leelavathamma, B and Nataraju, BS (2005) A functional representation for aiding biomimetic and artifical inspiration of new ideas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, 113–132. doi:10.1017/S0890060405050109.CrossRefGoogle Scholar
Chan, J, Fu, K, Schunn, C, Cagan, J, Wood, K and Kotovsky, K (2011) On the benefits and pitfalls of analogies for innovative design: ideation performance based on analogical distance, commonness, and modality of examples. Journal of Mechanical Design 133, 081004. doi:10.1115/1.4004396.CrossRefGoogle Scholar
Cheong, H and Shu, LH (2014) Retrieving causally related functions from natural-language text for biomimetic design. Journal of Mechanical Design 136, 081008. doi:10.1115/1.4027494.CrossRefGoogle Scholar
Chiu, I and Shu, LH (2012) Investigating effects of oppositely related semantic stimuli on design concept creativity. Journal of Engineering Design 23, 271–296. doi:10.1080/09544828.2011.603298.CrossRefGoogle Scholar
Choo, J, Lee, C, Reddy, CK and Park, H (2013) UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Transactions on Visualization and Computer Graphics 19, 1992–2001. doi:10.1109/TVCG.2013.212.CrossRefGoogle ScholarPubMed
Christensen, BT and Schunn, CD (2005) Spontaneous access and analogical incubation effects. Creativity Research Journal 17, 207–220. doi:10.1207/s15326934crj1702&3_7.CrossRefGoogle Scholar
Christensen, BT and Schunn, CD (2007) The relationship of analogical distance to analogical function and preinventive structure: the case of engineering design. Memory & Cognition 35, 29–38. doi:10.3758/Bf03195939.CrossRefGoogle ScholarPubMed
Chrysikou, EG and Weisberg, RW (2005) Following the wrong footsteps: fixation effects of pictorial examples in a design problem-solving task. Journal of Experimental Psychology: Learning Memory and Cognition 31, 1134–1148. doi:10.1037/0278-7393.31.5.1134.Google Scholar
Cichocki, A and Phan, AH (2009) Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E92a, 708–721. doi:10.1587/transfun.E92.A.708.CrossRefGoogle Scholar
Cross, N (2004) Expertise in design: an overview. Design Studies 25, 427–441. doi:10.1016/j.destud.2004.06.002.CrossRefGoogle Scholar
Dahl, DW and Moreau, P (2002) The influence and value of analogical thinking during new product ideation. Journal of Marketing Research 39, 47–60. doi:10.1509/jmkr.39.1.47.18930.CrossRefGoogle Scholar
deGroot, A (1978) Thought and Choice in Chess, 2nd edn. The Hague, The Netherlands: Mouton Publishers.Google Scholar
Deldin, J and Schuknecht, M (2014) The AskNature database: enabling solutions in biomimetic design. Biologically Inspired Design, 17–27. doi:10.1007/978-1-4471-5248-4_2.CrossRefGoogle Scholar
Du, RD, Kuang, D, Drake, B and Park, H (2017) DC-NMF: nonnegative matrix factorization based on divide-and-conquer for fast clustering and topic modeling. Journal of Global Optimization 68, 777–798. doi:10.1007/s10898-017-0515-z.CrossRefGoogle Scholar
Duckett, J (2010) Beginning HTML, XHTML, CSS, and JavaScript. Hoboken, NJ: Wiley.Google Scholar
Enkel, E and Gassmann, O (2010) Creative imitation: exploring the case of cross-industry innovation. R&D Management 40, 256–270. doi:10.1111/j.1467-9310.2010.00591.x.Google Scholar
Ericsson, KA, Nandagopal, K and Roring, R (2005) Giftedness viewed from the expert-performance perspective. Journal for the Education of the Gifted 28, 287. doi:10.4219/jeg-2005-335.CrossRefGoogle Scholar
Fricke, G (1996) Successful individual approaches in engineering design. Research in Engineering Design – Theory Applications and Concurrent Engineering 8, 151–165. doi:10.1007/Bf01608350.Google Scholar
Fu, K, Cagan, J, Kotovsky, K and Wood, K (2013 a) Discovering structure in design databases through functional and surface based mapping. Journal of Mechanical Design 135, 031006. doi:10.1115/1.4023484.CrossRefGoogle Scholar
Fu, K, Chan, J, Cagan, J, Kotovsky, K, Schunn, C and Wood, K (2013 b) The meaning of “near” and “far”: the impact of structuring design databases and the effect of distance of analogy on design output. Journal of Mechanical Design 135, 021007. doi:10.1115/1.4023158.CrossRefGoogle Scholar
Gentner, D (1988) Structure-Mapping: A Theoretical Framework for Analogy. Cognitive Sciecne 7, 155–170. https://doi.org/10.1016/S0364-0213(83)80009-3CrossRefGoogle Scholar
Gentner, D and Markman, AB (1997) Structure mapping in analogy and similarity. American Psychologist 52, 45–56. doi:10.1037/0003-066x.52.1.45.CrossRefGoogle Scholar
Glier, MW, McAdams, DA and Linsey, JS (2014) Exploring automated text classification to improve keyword corpus search results for bioinspired design. Journal of Mechanical Design 136, 111103. doi:10.1115/1.4028167.CrossRefGoogle Scholar
Goel, AK, Vattam, S, Wiltgen, B and Helms, M (2012) Cognitive, collaborative, conceptual and creative – four characteristics of the next generation of knowledge-based CAD systems: a study in biologically inspired design. Computer-Aided Design 44, 879–900. doi:10.1016/j.cad.2011.03.010.CrossRefGoogle Scholar
Goldschmidt, G and Smolkov, M (2006) Variances in the impact of visual stimuli on design problem solving performance. Design Studies 27, 549–569. doi:10.1016/j.destud.2006.01.002.CrossRefGoogle Scholar
Goncalves, M, Cardoso, C and Badke-Schaub, P (2013) Inspiration peak: exploring the semantic distance between design problem and textual inspirational stimuli. International Journal of Design Creativity and Innovation 1, 215–232. doi:10.1080/21650349.2013.799309.CrossRefGoogle Scholar
Greene, D, O'Callaghan, D and Cunningham, P (2014) How Many Topics? Stability Analysis for Topic Models, Vol. 8724. Berlin, Heidelberg: Springer.Google Scholar
Hanifan, R (ed.) (2014) Concise Dictionary of Engineering: A Guide to the Language of Engineering. Cham, Switzerland: Springer International Publishing.CrossRefGoogle Scholar
Hyslop, B 2010) HTML Basics. In Wimpsett, K (ed.), The HTML Pocket Guide. Berkeley, CA: Peachpit Press.Google Scholar
Jansson, DG and Smith, SM (1991) Design fixation. Design Studies 12, 3–11. doi:10.1016/0142-694X(91)90003-F.CrossRefGoogle Scholar
Kang, IS, Na, SH, Kim, J and Lee, JH (2007) Cluster-based patent retrieval. Information Processing & Management 43, 1173–1182. doi:10.1016/j.ipm.2006.11.006.CrossRefGoogle Scholar
Keshwani, S and Chakrabarti, A (2017) Towards automatic classification of description of analogies into SAPPhIRE constructs. Research into Design for Communities 2, 643–655. doi:10.1007/978-981-10-3521-0_55.CrossRefGoogle Scholar
Kim, J and Park, H (2008) Toward faster nonnegative matrix factorization: a new algorithm and comparisons. ICDM 2008: Proceedings of Eighth IEEE International Conference on Data Mining, pp. 353–362. doi:10.1109/Icdm.2008.149.CrossRefGoogle Scholar
Kim, J and Park, H (2011) Fast nonnegative matrix factorization: an active-set-like method and comparisons. SIAM Journal on Scientific Computing 33, 3261–3281. doi:10.1137/110821172.CrossRefGoogle Scholar
Koch, S, Bosch, H, Giereth, M and Ertl, T (2011) Iterative integration of visual insights during scalable patent search and analysis. IEEE Transactions on Visualization and Computer Graphics 17, 557–569. doi:10.1109/Tvcg.2010.85.CrossRefGoogle ScholarPubMed
Kuang, D and Park, H (2013) Fast rank-2 nonnegative matrix factorization for hierarchical document clustering. Paper Presented at the Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA. pp. 739–747, https://doi.org/10.1145/2487575.2487606Google Scholar
Lee, DD and Seung, HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791. doi:10.1038/44565.CrossRefGoogle ScholarPubMed
Lee, H, Lee, BP and Messersmith, PB (2007) A reversible wet/dry adhesive inspired by mussels and geckos. Nature 448, 338–341. doi:10.1038/nature05968.CrossRefGoogle ScholarPubMed
Lim, W, Du, R and Park, H (2018) CoDiNMF: co-clustering of directed graphs via NMF. Proceedings of the Thirty-Second {AAAI} Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th {AAAI} Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018,pp. 3611-3618Google Scholar
Linsey, JS, Wood, KL and Markman, AB (2008) Modality and representation in analogy. Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing 22, 85–100. doi:10.1017/S0890060408000061.CrossRefGoogle Scholar
Linsey, JS, Markman, AB and Wood, KL (2012) Design by analogy: a study of the WordTree method for problem re-representation. Journal of Mechanical Design 134, 041009. doi:10.1115/1.4006145.CrossRefGoogle Scholar
Lucero, B, Turner, CJ and Linsey, J (2016) Design repository & analogy computation via unit language analysis (DRACULA) repository development. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2015, Vol. 1a.CrossRefGoogle Scholar
Malaga, RA (2000) The effect of stimulus modes and associative distance in individual creativity support systems. Decision Support Systems 29, 125–141. doi:10.1016/S0167-9236(00)00067-1.CrossRefGoogle Scholar
Montecchi, T, Russo, D and Liu, Y (2013) Searching in Cooperative Patent Classification: comparison between keyword and concept-based search. Advanced Engineering Informatics 27, 335–345. doi:10.1016/j.aei.2013.02.002.CrossRefGoogle Scholar
Mozilla Developer (2019) Introduction to the DOM. Retrieved from https://developer.mozilla.org/enUS/docs/Web/API/Document_Object_Model/IntroductionGoogle Scholar
Mukherjea, S, Bamba, B and Kankar, P (2005) Information retrieval and knowledge discovery utilizing a biomedical patent semantic Web. IEEE Transactions on Knowledge and Data Engineering 17, 1099–1110. doi:10.1109/Tkde.2005.130.Google Scholar
Murphy, J (2011) Patent-Based Analogy Search Tool for Innovative Concept Generation (PhD). The University of Texas at Austin, Austin, TX.Google Scholar
Murray, S (2017) Interactive Data Visualization for the Web: An Introduction to Designing with D3, 2nd edn. Beijing; Boston: O'Reilly.Google Scholar
Nagel, JKS, Stone, RB and McAdams, DA (2010) An engineering-to-biology thesaurus for engineering design. Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, December 2010, Vol. 5, pp. 117–128.CrossRefGoogle Scholar
Paatero, P and Tapper, U (1994) Positive matrix factorization – a nonnegative factor model with optimal utilization of error-estimates of data values. Environmetrics 5, 111–126. doi:10.1002/env.3170050203.CrossRefGoogle Scholar
Pauca, VP, Shahnaz, F, Berry, MW and Plemmons, RJ (2004) Text mining using non-negative matrix factorizations. Proceedings of the Fourth SIAM International Conference on Data Mining, pp. 452–456.CrossRefGoogle Scholar
Philippe Le Hégaret, WC, Wood, L, SoftQuad Software Inc., WG Chair and Jonathan Robie (2000). What is the document object model? Retrieved from https://www.w3.org/TR/DOM-Level-2-Core/introduction.htmlGoogle Scholar
Purcell, AT and Gero, J (1996) Design and other types of fixation. Design Studies 17, 363–383. doi:10.1016/S0142-694X(96)00023-3.CrossRefGoogle Scholar
Razzouk, R and Shute, V (2012) What is design thinking and why is it important?. Review of Educational Research 82, 330–348. doi:10.3102/0034654312464201.CrossRefGoogle Scholar
Reid, J (2013) JavaScript Programmer's Reference. Berkeley, CA: Apress. Imprint: Apress.CrossRefGoogle Scholar
Sarkar, S (2014) Why frame rate and resolution matter: a graphics primer. Retrieved from https://www.polygon.com/2014/6/5/5761780/frame-rate-resolution-graphics-primer-ps4-xbox-oneGoogle Scholar
Shah, JJ, Vargas-Hernandez, N and Smith, SM (2003) Metrics for measuring ideation effectiveness. Design Studies 24, 111–134. doi:10.1016/S0142-694X(02)00034-0.CrossRefGoogle Scholar
Smith, SM and Blankenship, SE (1991) Incubation and the persistence of fixation in problem-solving. American Journal of Psychology 104, 61–87. doi:10.2307/1422851.CrossRefGoogle ScholarPubMed
Song, H and Fu, K (2019) Design-by-analogy: exploring for analogical inspiration with behavior, material, and component-based structural representation of patent databases. Journal of Computing and Information Science in Engineering 19, 021014. doi:10.1115/1.4043364.Google Scholar
Song, B and Luo, J (2017) Mining patent precedents for data-driven design: the case of spherical rolling robots. Journal of Mechanical Design 139, 111420. doi:10.1115/1.4037613.CrossRefGoogle Scholar
Tseng, I, Moss, J, Cagan, J and Kotovsky, K (2008) The role of timing and analogical similarity in the stimulation of idean generation in design. Design Studies 29, 203–221.CrossRefGoogle Scholar
Ullman, D (2003) The Mechanical Design Process, 3rd edn. Boston, MA: McGraw-Hill.Google Scholar
Vandevenne, D, Verhaegen, PA, Dewulf, S and Duflou, JR (2016) SEABIRD: scalable search for systematic biologically inspired design. Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing 30, 78–95. doi:10.1017/S0890060415000177.CrossRefGoogle Scholar
Venkataraman, S and Chakrabarti, A (2009) SAPPhIRE: an approach to analysis and synthesis, Proceedings of ICED 09, the 17th International Conference on Engineering Design, vol.2, pp. 417-428Google Scholar
Vincent, JFV and Mann, DL (2002) Systematic technology transfer from biology to engineering. Philosophical Transactions of the Royal Society of London Series A – Mathematical Physical and Engineering Sciences 360, 159–173. doi:10.1098/rsta.2001.0923.CrossRefGoogle ScholarPubMed
Vosniadou, S and Ortony, A (1989) Similarity and Analogical Reasoning. Cambridge, New York: Cambridge University Press.Google Scholar
W3 (2017) HTML 5.2. Retrieved from https://www.w3.org/TR/2017/REC-html52-20171214/Google Scholar
World Intellectual Property Indicators (2017) Geneva, Switzerland: World Intellectual Property Organization, https://www.wipo.int/edocs/pubdocs/en/wipo_pub_941_2017.pdfGoogle Scholar
Xu, W, Liu, X and Gong, Y (2003) Document clustering based on non-negative matrix factorization. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, pp. 267–273, https://doi.org/10.1145/860435.860485Google Scholar