Our society has entered a data-driven era, in which not only enormous amounts of data are being generated every day, but there are also growing expectations placed on their analysis. Exploring these massive and complex datasets is essential to making new discoveries and creating benefits for people, but it remains a very difficult task; most data have become simply too large and often have too short a lifespan, i.e. they change too rapidly, for classical visualization or analysis methods to handle it properly. This simple observation has leaded my researches to investigate accurate and fast tools for multivariate data exploration and management. During these past years, I investigated several application domains: air traffic control, medical visualizations, trajectory visualization, image processing, software visualization, personal computer management. For each of these application domains, I developed interactions techniques and data processing algorithms: Skeleton, Kernel Density Estimation and attribute basedEdge Bundling (SBEB, KDEEB, ADEB), trajectory spreading interaction, animated transitions, augmented reality visualizations. These algorithms and interactive techniques has been integrated and tested into different software: FromDaDy, MoleView, Active Progressbar, Strip’Tic, ColorTunneling. These developed algorithms and interactive techniques share a same technical specificity: they embed pixel based techniques. As such, data (i.e. decimal values) can be handled in their original dimensions, or they can be projected into different one. With pixel based technique, data are projected into raster maps (i.e. integer values). This collection of techniques trends to bridge the gap between GeoVis, InfoVis, SciVis and Visual Analytics by providing a common set of interactive tools. Even if these techniques have proven to be efficient, many questions remain. In this document, I will reflect upon the validity of these pixels based techniques in terms of data accuracy, scalability, limitations, domain applicability and I will finally try to envisage their future usages. Keywords: InfoVis, GP-GPU, Pixel Based Techniques, Interactive Visualization, Demos, Air traffic Control, Edge Bundling, Hololens<\/p>\n","protected":false},"excerpt":{"rendered":"
Our society has entered a data-driven era, in which not only enormous amounts of data are being generated every day, but there are also growing expectations placed on their analysis. Exploring these massive and complex datasets is essential to making new discoveries and creating benefits for people, but it remains a very difficult task; most […]<\/p>\n","protected":false},"featured_media":302660,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13561,13563],"msr-video-type":[206954],"msr-locale":[268875],"msr-post-option":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-302633","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-algorithms","msr-research-area-data-platform-analytics","msr-video-type-microsoft-research-talks","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/ajgxJXIgdQI","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/302633"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/302633\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/302660"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=302633"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=302633"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=302633"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=302633"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=302633"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=302633"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=302633"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}