Exploring correlational information in aggregated quantified self data dashboards

by | Sep 1, 2015 | Research Publications

Author

Simon L. Jones

Abstract:

Data aggregation platforms are often depicted as a panacea for users wanting to examine correlations within the multi-faceted data that they collect. In this paper we describe inherent challenges with the provision of multi-faceted, correlational information in data aggregation tools, and present a set of hypotheses related to these challenges. We point to design considerations for improving such tools and describe an on-going study of one such tool, Exist.io, in which we aim to explore the issues discussed.

Document:

https://doi.org/10.1145/2800835.2800963

References:
  1. Eun Kyoung Choe, Bongshin Lee, and M. C. Schraefel. (2015). Revealing Visualization Insights from Quantified-Selfers’ Personal Data Presentations, Computer Graphics and Applications, IEEE, vol. PP, no.99, pp.1,1.
  2. Eun Kyoung Choe, Nicole B. Lee, Bongshin Lee, Wanda Pratt, and Julie A. Kientz. (2014, April). Understanding quantified-selfers’ practices in collecting and exploring personal data. In Proc. 32nd annual ACM conference on Human factors in computing systems (pp. 1143–1152).
  3. Emily Collins, Jon Bird, Anna L. Cox, and Daniel Harrison. (2014). Social networking use and RescueTime: The issue of engagement. In Proc. 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. (pp. 687–690).
  4. Tilman Dingler, Alireza Sahami and Niels Henze. (2014). There is More to Well-being than Health Data: Holistic Lifelogging through Memory Capture. CHI 2014 – Workshop Beyond Quantified Self: Data for Wellbeing
  5. Stephen Few. (2006). “Common Pitfalls in Dashboard Design.” Perceptual Edge.
  6. Hamed Haddadi and Ian Brown. (2014). Quantified self and the privacy challenge. Technology Law Futures.
  7. Ian Li, Anind Dey, and Jodi Forlizzi. (2010, April). A stage-based model of personal informatics systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 557–566). ACM.
  8. Ian Li, Jon Froehlich, Jakob E. Larsen, Catherine Grevet, and Ernesto Ramirez. (2013). Personal informatics in the wild: hacking habits for health & happiness. InCHI’13 Extended Abstracts on Human Factors in Computing Systems (pp. 3179–3182). ACM.
  9. Ian Li. (2015). Beyond Reflecting on Personal Data: Predictive Personal Informatics. CHI 2015 Workshop – Beyond Personal Informatics: Designing for Experiences with Data.
  10. Shalom H. Schwartz. (2012). An overview of the Schwartz theory of basic values. Online Readings in Psychology and Culture, 2(1), 11.
  11. Christopher Trickler. (2013). An overview of self-monitoring systems. In Proc. Southern Association for Information Systems Conference (pp. 197–202).
  12. Konrad Tollmar, Frank Bentley, Cristobal Viedma, and I. L. Libertyville. (2012). “Mobile health mashups.” Proc. PervasiveHealth 12.
  13. Hongbin Zhuang. (2014). Was I productive? Examining the reliability of the Quantified Self technology. Faculty of Brain Sciences, University College London.