{"id":722047,"date":"2021-02-02T06:38:05","date_gmt":"2021-02-02T14:38:05","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=722047"},"modified":"2021-02-02T06:38:05","modified_gmt":"2021-02-02T14:38:05","slug":"cast-authoring-data-driven-chart-animations","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/cast-authoring-data-driven-chart-animations\/","title":{"rendered":"CAST: Authoring Data-Driven Chart Animations"},"content":{"rendered":"
We present CAST, an authoring tool that enables the interactive creation of chart animations. It introduces the visual specification of chart animations consisting of keyframes that can be played sequentially or simultaneously, and animation parameters (e.g., duration, delay). Building on Canis, a declarative chart animation grammar that leverages data-enriched SVG charts, CAST supports auto-completion for constructing both keyframes and keyframe sequences. It also enables users to refine the animation specification (e.g., aligning keyframes across tracks to play them together, adjusting delay) with direct manipulation and other parameters for animation effects (e.g., animation type, easing function) using a control panel. In addition to describing how CAST infers recommendations for auto-completion, we present a gallery of examples to demonstrate the expressiveness of CAST and a user study to verify its learnability and usability. Finally, we discuss the limitations and potentials of CAST as well as directions for future research.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present CAST, an authoring tool that enables the interactive creation of chart animations. It introduces the visual specification of chart animations consisting of keyframes that can be played sequentially or simultaneously, and animation parameters (e.g., duration, delay). Building on Canis, a declarative chart animation grammar that leverages data-enriched SVG charts, CAST supports auto-completion for […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563,13554],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-722047","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-5-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/01\/CAST-CHI2021.pdf","id":"722635","title":"cast-chi2021","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":722635,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/02\/CAST-CHI2021.pdf"}],"msr-author-ordering":[{"type":"text","value":"Tong Ge","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Bongshin Lee","user_id":31276,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Bongshin Lee"},{"type":"text","value":"Yunhai Wang","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[371909,379814],"msr_project":[365414],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":365414,"post_title":"Data-Driven Storytelling","post_name":"data-driven-storytelling","post_type":"msr-project","post_date":"2017-02-22 09:33:17","post_modified":"2022-05-30 10:49:49","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/data-driven-storytelling\/","post_excerpt":"Practitioners increasingly use visualizations \u201cin the wild\u201d to tell compelling stories supported by data, and continually develop novel techniques that help integrate data visualization into narrative stories. 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