FacetLens: Exposing Trends and Relationships to Support Sensemaking within Faceted Datasets

Bongshin Lee, Greg Smith, George G. Robertson, Mary Czerwinski, and Desney S. Tan

Abstract

Previous research has shown that faceted browsing is effective and enjoyable in searching and browsing large collections of data. In this work, we explore the efficacy of interactive visualization systems in supporting exploration and sensemaking within faceted datasets. To do this, we developed an interactive visualization system called FacetLens, which exposes trends and relationships within faceted datasets. FacetLens implements linear facets to enable users not only to identify trends but also to easily compare several trends simultaneously. Furthermore, it offers pivot operations to allow users to navigate the faceted dataset using relationships between items. We evaluate the utility of the system through a description of insights gained while experts used the system to explore the CHI publication repository as well as a database of funding grant data, and report a formative user study that identified usability issues.

Details

Publication typeProceedings
PublisherAssociation for Computing Machinery, Inc.
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