Dissertation/Thesis Abstract

Human-Data Interaction in Large and High-Dimensional Data
by Amraii, Saman Amirpour, Ph.D., University of Pittsburgh, 2017, 192; 10831835
Abstract (Summary)

Human-Data Interaction (HDI) is an emerging field which studies how humans make sense of large and complex data. Visual analytics tools are a central component of this sensemaking process. However, the growth of big data has affected their performance, resulting in latency in interactivity or long query-response times, both of which degrade one's ability to do knowledge discovery. To address these challenges, a new paradigm of data exploration has appeared in which a rapid but inaccurate result is followed by a succession of gradually more accurate answers. As the primary objective of this thesis, we investigated how this incremental latency affects the quantity and quality of knowledge discovery in an HDI system. We have developed a big data visualization tool and studied 40 participants in a think-aloud experiment, using this tool to explore a large and high-dimensional data. Our findings indicate that although incremental latency reduces the rate of discovery generation, it does not affect one's chance of making a discovery per each generated visualization, and it does not affect the correctness of those discoveries. However, in the presence of latency, utilizing contextual layers such as a map result in fewer mistakes while exploring higher-dimensional visualizations lead to more incorrect discoveries. As the secondary objective, we investigated what strategies improved a subject's performance. Our observations suggest that successful participants explore the data methodically, by first examining simple and familiar concepts and then gradually adding complexity to the visualizations, until they build a correct mental model of the inner workings of the tool. With this model, they generate several discovery patterns, each acting as a blueprint for forming new insights. Ultimately, some participants combined their discovery patterns to create multifaceted data-driven stories. Based on these observations, we propose design guidelines for developing HDI platforms for large and high-dimensional data.

Indexing (document details)
Advisor:
Commitee:
School: University of Pittsburgh
Department: Intelligent Systems Program
School Location: United States -- Pennsylvania
Source: DAI-B 79/09(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Computer Engineering, Computer science
Keywords: Big data, Human-computer interaction, Human-data interaction, Knowledge discovery, Visual analytics, Visualization
Publication Number: 10831835
ISBN: 9780355886726
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