Abstract
This paper will discuss how the use of an analytical framework in conjunction with the current human interface, improved the understanding of the International Space Station (ISS) crew perspective data and shortened analysis time, allowing for more informed decisions and rapid development of improvements. These data analytics and visualization methods significantly optimize valuable ISS postflight crew debrief qualitative data, yielding results that can be applied to both current and future spaceflight design and development, and other domains. This paper will discuss a collaboration that has allowed a team of Human Factors engineers at NASA’s Johnson Space Center (JSC) to analyze and share data in a more automated and accurate fashion, thanks to the efforts of the JSC Chief Knowledge Office (CKO). Trends are no longer manually derived and are visualized effectively to assist in presenting these evolving techniques and subsequent results to an ever-growing population of human spaceflight end users.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Evans, M.S.: A computational approach to qualitative analysis in large textual datasets. PLoS ONE 9(2), 1–10 (2014)
Tanguy, L., Tulechki, N., Urieli, A., Hermann, E., Raynal, C.: Natural language processing for aviation safety reports: from classification to interactive analysis. Comput. Ind. 78, 80–95 (2016)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Opinion Lexicon, pp. 168–177. ACM, New York (2004). https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
Rinker, T.: qdap Quantitative Discourse Analysis Package, University at Buffalo/SUNY, Buffalo, New York (2013). http://github.com/trinker/qdap
Ogneva, M.: How Companies Can Use Sentiment Analysis to Improve Their Business. Mashable (2010). Accessed 3 Jan 2017
Feinerer, I., Hornik, K.: tm: Text Mining Package, R Package version 0.6-2 (2015). http://CRAN.R-project.org/package=tm
Paradis, E., Claude, J., Strimmer, K.: APE: analyses of phylogenetics and evolution in R laguage. Bioinformatics 20, 289–290 (2004)
Murtagh, F.: Multidimensional Clustering Algorithms, COMPSTAT Lectures 4. Physica-Verlag, Vienna (1985)
Ward Jr., J.H.: Hierarchical grouping to optimize on objective function. J. Am. Statistical Association 58(301), 236–244 (1963)
Cleveland, W.S., Grosse, E., Shyu, W.M.: Local regression models. In: Chambers, J.M., Hastie, T.J. (eds.) Statistical Models in S. Wadsworth & Brooks/Cole (1992). Chap. 8
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Acknowledgments
The authors thank Katherine Vasser of MEI Technologies (NASA JSC contractor) and Rhonda Russo of JES Tech (NASA JSC contractor) for their expertise and assistance with collection and processing of the Crew Comments data. The authors also thank Laura Duvall and John McBrine of NASA JSC for their guidance and support concerning the Crew Comments data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Bryant, C., Schuh, S., Schoenstein, N., Meza, D. (2018). Understanding the International Space Station Crew Perspective Following Long Duration Missions Through Data Analytics and Visualization of Crew Feedback. In: Ahram, T., Falcão, C. (eds) Advances in Usability and User Experience. AHFE 2017. Advances in Intelligent Systems and Computing, vol 607. Springer, Cham. https://doi.org/10.1007/978-3-319-60492-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-60492-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60491-6
Online ISBN: 978-3-319-60492-3
eBook Packages: EngineeringEngineering (R0)