Situated Analytics for Data Scientists

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Date

2022

Authors

Batch, Andrea

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Abstract

Much of Mark Weiser's vision of ubiquitous computing'' has come to fruition: We live in a world of interfaces that connect us with systems, devices, and people wherever we are. However, those of us in jobs that involve analyzing data and developing software find ourselves tied to environments that limit when and where we may conduct our work; it is ungainly and awkward to pull out a laptop during a stroll through a park, for example, but difficult to write a program on one's phone. In this dissertation, I discuss the current state of data visualization in data science and analysis workflows, the emerging domains of immersive and situated analytics, and how immersive and situated implementations and visualization techniques can be used to support data science. I will then describe the results of several years of my own empirical work with data scientists and other analytical professionals, particularly (though not exclusively) those employed with the U.S. Department of Commerce. These results, as they relate to visualization and visual analytics design based on user task performance, observations by the researcher and participants, and evaluation of observational data collected during user sessions, represent the first thread of research I will discuss in this dissertation. I will demonstrate how they might act as the guiding basis for my implementation of immersive and situated analytics systems and techniques. As a data scientist and economist myself, I am naturally inclined to want to use high-frequency observational data to the end of realizing a research goal; indeed, a large part of my research contributions---and a second thread'' of research to be presented in this dissertation---have been around interpreting user behavior using real-time data collected during user sessions. I argue that the relationship between immersive analytics and data science can and should be reciprocal: While immersive implementations can support data science work, methods borrowed from data science are particularly well-suited for supporting the evaluation of the embodied interactions common in immersive and situated environments. I make this argument based on both the ease and importance of collecting spatial data from user sessions from the sensors required for immersive systems to function that I have experienced during the course of my own empirical work with data scientists. As part of this thread of research working from this perspective, this dissertation will introduce a framework for interpreting user session data that I evaluate with user experience researchers working in the tech industry.

Finally, this dissertation will present a synthesis of these two threads of research. I combine the design guidelines I derive from my empirical work with machine learning and signal processing techniques to interpret user behavior in real time in Wizualization, a mid-air gesture and speech-based augmented reality visual analytics system.

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