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Assessment of Expert Interaction with Multivariate Time Series ‘Big Data’

  • Susan Stevens AdamsEmail author
  • Michael J. Haass
  • Laura E. Matzen
  • Saskia King
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9744)

Abstract

‘Big data’ is a phrase that has gained much traction recently. It has been defined as ‘a broad term for data sets so large or complex that traditional data processing applications are inadequate and there are challenges with analysis, searching and visualization’ [1]. Many domains struggle with providing experts accurate visualizations of massive data sets so that the experts can understand and make decisions about the data e.g., [2, 3, 4, 5].

Abductive reasoning is the process of forming a conclusion that best explains observed facts and this type of reasoning plays an important role in process and product engineering. Throughout a production lifecycle, engineers will test subsystems for critical functions and use the test results to diagnose and improve production processes.

This paper describes a value-driven evaluation study [7] for expert analyst interactions with big data for a complex visual abductive reasoning task. Participants were asked to perform different tasks using a new tool, while eye tracking data of their interactions with the tool was collected. The participants were also asked to give their feedback and assessments regarding the usability of the tool. The results showed that the interactive nature of the new tool allowed the participants to gain new insights into their data sets, and all participants indicated that they would begin using the tool in its current state.

Keywords

Big data Eye tracking Evaluation study Knowledge elicitation 

Notes

Acknowledgements

Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. SAND NO. 2016-1724C. This work was funded by Laboratory Directed Research and Development (LDRD).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Susan Stevens Adams
    • 1
    Email author
  • Michael J. Haass
    • 1
  • Laura E. Matzen
    • 1
  • Saskia King
    • 1
  1. 1.Sandia National LaboratoriesAlbuquerqueUSA

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