| id |
caadria2017_086 |
| authors |
Koh, Immanuel, Keel, Paul and Huang, Jeffrey |
| year |
2017 |
| title |
Decoding Parametric Design Data - Towards a Heterogeneous Design Search Space Remix |
| doi |
https://doi.org/10.52842/conf.caadria.2017.117
|
| source |
P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (eds.), Protocols, Flows, and Glitches - Proceedings of the 22nd CAADRIA Conference, Xi'an Jiaotong-Liverpool University, Suzhou, China, 5-8 April 2017, pp. 117-126 |
| summary |
Designers or Non-Designers are not able to effectively access, view, search, discover, collect, reuse, remix and share parametric design data (PDD) for either professional or educational purposes. PDD here refers to the meta-data of 3D models generated by visual dataflow modelling software packages used in CAD/CAM industry. This ineffectiveness is a direct consequence of the deliberately proprietary nature of most PDD file formats and the restricted use within their respective desktop-based software environments. This paper presents an initial software prototype capable of automating the process of decoding a commonly used PDD file format and then re-encoding it with new set of metrics to facilitate multiple PDD searchability, comparability and interoperability, via an integrated web interface querying a design data repository. All PDDs are conceptualized as genealogies of numerical or geometric transformations and explicitly encoded with a graph-based data structure. The goal is to eventually learn from its own big data and begin to artificially generate novel PDDs heterogeneously. |
| keywords |
Design Decoder; Design Space Exploration; Parametric Design; Visual Analytics; Design Data |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (8,912,736 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:51 |
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