[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”3.22″][et_pb_row _builder_version=”3.22.1″ border_color_all=”#3255c9″ border_style_all=”groove”][et_pb_column type=”1_2″ _builder_version=”3.22.1″][et_pb_text _builder_version=”3.22.1″ min_height=”11px” custom_margin=”||-25px|||” custom_padding=”||0px|||”]<\/p>\n
Eamonn Keogh<\/span><\/p>\n February 2, 2022, at 4 PM<\/span><\/p>\n Online (Zoom)<\/span><\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.22.1″][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding=”44px|||||” custom_margin=”80px||80px” _builder_version=”3.22.1″][et_pb_column type=”2_3″ _builder_version=”3.22.1″][et_pb_text _builder_version=”3.22.1″ custom_margin=”-51px|||||” custom_padding=”0px|||||”]<\/p>\n <\/p>\n Time series data mining is the task of finding patterns, regularities, and outliers in massive datasets. Given the ubiquity of time series in medicine, science, and industry, time series data mining is of increasing importance. In this talk I shall argue that the simple primitive of time series motif discovery, the task of finding approximately repeated patterns with a dataset, is the most useful core operation in all of time series data mining. In particular, it can be used as a primitive to enable many other useful tasks, such as summarization, segmentation, classification, clustering and anomaly detection. I will argue my case with examples of motif discovery in datasets as diverse as penguin behavior, cardiology, and astronomy.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”3.22.1″][et_pb_video_slider _builder_version=”3.22.1″][et_pb_video_slider_item src=”https:\/\/www.youtube.com\/watch?v=BYjOp2NoDdc” src_webm=”https:\/\/www.youtube.com\/watch?v=BYjOp2NoDdc” _builder_version=”3.22.1″ show_image_overlay=”off”][\/et_pb_video_slider_item][\/et_pb_video_slider][et_pb_text admin_label=”Text” _builder_version=”3.22.1″]<\/p>\n Click the image below to see slides<\/em><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-content\/uploads\/sites\/27\/2024\/10\/Screenshot-2024-10-23-151556.png” url=”https:\/\/drive.google.com\/file\/d\/1ZkdxlY4U9pgpm6W_KEQtbbVAocMtqr4g\/view” _builder_version=”3.22.1″ transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.22.1″][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”1_3″ _builder_version=”3.22.1″][et_pb_image src=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-content\/uploads\/sites\/27\/2021\/11\/eamonn20keogh.jpeg” align=”center” _builder_version=”3.22.1″ custom_margin=”20px|125px||||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”3.22.1″][et_pb_text _builder_version=”3.22.1″ custom_padding=”0px|||||”]<\/p>\n Eamonn Keogh is a distinguished professor and Ross Family Chair in the Department of Computer Science and Engineering. He specializes in time series data mining, finding patterns, regularities, and outliers in massive datasets. He developed some of the most commonly used definitions, algorithms and data representations used in this area. These contributions include SAX, PAA, Time Series Shapelets, Time Series Motifs, the LBkeogh lower bound, and the Matrix Profile. These ideas have been used by thousands of academic, industrial, and scientific researchers worldwide, including NASA\u2019s Jet Propulsion Laboratory, which uses Keogh\u2019s ideas to find anomalies in observations of the magnetosphere collected by the Cassini spacecraft in orbit around Saturn. In the week following this talk, he will be presented with the 2021 IEEE ICDM Research Contributions Award.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_margin=”120px||” _builder_version=”3.22.1″ locked=”off”][et_pb_column type=”4_4″ _builder_version=”3.0.47″][et_pb_divider _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text admin_label=”\u00c0 lire aussi” _builder_version=”3.22.1″ z_index_tablet=”500″]<\/p>\n [\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”65″ show_author=”off” show_date=”off” show_pagination=”off” module_id=”page_type_blog” _builder_version=”3.22.1″ header_level=”h4″ border_width_bottom_fullwidth=”1px” border_color_bottom_fullwidth=”rgba(51,51,51,0.18)” custom_padding=”||50px|” z_index_tablet=”500″][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Eamonn Keogh February 2, 2022, at 4 PM Online (Zoom) Abstract Time series data mining is the task of finding patterns, regularities, and outliers in massive datasets. Given the ubiquity of time series in medicine, science, and industry, time series data mining is of increasing importance. In this talk I shall argue that the… Lire la suite Eamonn Keogh – Finding Approximately Repeated Patterns in Time Series: The most Useful, and yet most Underutilized Primitive in Time Series Analytics<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":710,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[55,1,65],"tags":[],"class_list":["post-2690","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-55","category-diip","category-distinguished-lectures"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2690","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/comments?post=2690"}],"version-history":[{"count":3,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2690\/revisions"}],"predecessor-version":[{"id":2696,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2690\/revisions\/2696"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media\/710"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=2690"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=2690"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=2690"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Abstract<\/strong><\/h3>\n
Prof. Eamonn Keogh
<\/strong>(University of California Riverside)<\/p>\n
<\/strong><\/h3>\nOther distinguished lectures
<\/span><\/h2>\n