Browsing by Author "Munzner, Tamara"

Now showing 1 - 3 of 3
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    Ocupado: Visualizing Location-Based Counts Over Time Across Buildings
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Oppermann, Michael; Munzner, Tamara; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Understanding how spaces in buildings are being used is vital for optimizing space utilization, for improving resource allocation, and for the design of new facilities. We present a multi-year design study that resulted in Ocupado, a set of visual decision-support tools centered around occupancy data for stakeholders in facilities management and planning. Ocupado uses WiFi devices as a proxy for human presence, capturing location-based counts that preserve privacy without trajectories. We contribute data and task abstractions for studying space utilization for combinations of data granularities in both space and time. In addition, we contribute generalizable design choices for visualizing location-based counts relating to indoor environments. We provide evidence of Ocupado's utility through multiple analysis scenarios with real-world data refined through extensive stakeholder feedback, and discussion of its take-up by our industry partner.
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    Segmentifier: Interactive Refinement of Clickstream Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Dextras-Romagnino, Kimberly; Munzner, Tamara; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Clickstream data has the potential to provide insights into e-commerce consumer behavior, but previous techniques fall short of handling the scale and complexity of real-world datasets because they require relatively clean and small input. We present Segmentifier, a novel visual analytics interface that supports an iterative process of refining collections of action sequences into meaningful segments. We present task and data abstractions for clickstream data analysis, leading to a high-level model built around an iterative view-refine-record loop with outcomes of conclude with an answer, export segment for further analysis in downstream tools, or abandon the question for a more fruitful analysis path. Segmentifier supports fast and fluid refinement of segments through tightly coupled visual encoding and interaction with a rich set of views that show evocative derived attributes for segments, sequences, and actions in addition to underlying raw sequences. These views support fast and fluid refinement of segments through filtering and partitioning attribute ranges. Interactive visual queries on custom action sequences are aggregated according to a three-level hierarchy. Segmentifier features a detailed glyph-based visual history of the automatically recorded analysis process showing the provenance of each segment as an analysis path of attribute constraints. We demonstrate the effectiveness of our approach through a usage scenario with real-world data and a case study documenting the insights gained by a corporate e-commerce analyst.
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    SepEx: Visual Analysis of Class Separation Measures
    (The Eurographics Association, 2020) Bernard, Jürgen; Hutter, Marco; Zeppelzauer, Matthias; Sedlmair, Michael; Munzner, Tamara; Turkay, Cagatay and Vrotsou, Katerina
    Class separation is an important concept in machine learning and visual analytics. However, the comparison of class separation for datasets with varying dimensionality is non-trivial, given a) the various possible structural characteristics of datasets and b) the plethora of separation measures that exist. Building upon recent findings in visualization research about the qualitative and quantitative evaluation of class separation for 2D dimensionally reduced data using scatterplots, this research addresses the visual analysis of class separation measures for high-dimensional data. We present SepEx, an interactive visualization approach for the assessment and comparison of class separation measures for multiple datasets. SepEx supports analysts with the comparison of multiple separation measures over many high-dimensional datasets, the effect of dimensionality reduction on measure outputs by supporting nD to 2D comparison, and the comparison of the effect of different dimensionality reduction methods on measure outputs. We demonstrate SepEx in a scenario on 100 two-class 5D datasets with a linearly increasing amount of separation between the classes, illustrating both similarities and nonlinearities across 11 measures.