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FacetLens: exposing trends and relationships to support sensemaking within faceted datasets

Published: 04 April 2009 Publication History

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.

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    cover image ACM Conferences
    CHI '09: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
    April 2009
    2426 pages
    ISBN:9781605582467
    DOI:10.1145/1518701
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 04 April 2009

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    Author Tags

    1. facets
    2. interactive visualization
    3. relationships
    4. sensemaking
    5. trends

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    CHI '09 Paper Acceptance Rate 277 of 1,130 submissions, 25%;
    Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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    • (2024)Dataopsy: Scalable and Fluid Visual Exploration using Aggregate Query SculptingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332659430:1(186-196)Online publication date: 1-Jan-2024
    • (2022)Contextual in situ help for visual data interfacesInformation Visualization10.1177/1473871622112006422:1(69-84)Online publication date: 9-Sep-2022
    • (2021)Comprehensive Review and Future Research Directions on Dynamic Faceted SearchApplied Sciences10.3390/app1117811311:17(8113)Online publication date: 31-Aug-2021
    • (2021)Visual Analysis of Multilayer NetworksSynthesis Lectures on Visualization10.2200/S01094ED1V01Y202104VIS0128:1(1-150)Online publication date: 8-Jun-2021
    • (2020)Orchard: Exploring Multivariate Heterogeneous Networks on Mobile PhonesComputer Graphics Forum10.1111/cgf.1396739:3(115-126)Online publication date: 18-Jul-2020
    • (2020)Review of Techniques in Faceted Search Applications2020 International Symposium on Networks, Computers and Communications (ISNCC)10.1109/ISNCC49221.2020.9297275(1-5)Online publication date: 20-Oct-2020
    • (2020)Evaluating geovisualization for spatial learning analyticsInternational Journal of Cartography10.1080/23729333.2020.1735034(1-19)Online publication date: 14-May-2020
    • (2020)Designing a Visual Analytics System for Medication Error Screening and DetectionComputer Vision, Imaging and Computer Graphics Theory and Applications10.1007/978-3-030-41590-7_12(285-312)Online publication date: 20-Feb-2020
    • (2019)The State of the Art in Multilayer Network VisualizationComputer Graphics Forum10.1111/cgf.1361038:6(125-149)Online publication date: 28-Mar-2019
    • (2019)A Visualization Technique to Support Searching FilteringMATEC Web of Conferences10.1051/matecconf/201925505003255(05003)Online publication date: 16-Jan-2019
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