Return-Path: Delivered-To: cadet@irif.fr Received: from mailhub.math.univ-paris-diderot.fr ([81.194.30.253]) by mailhost.irif.fr (Dovecot) with LMTP id GbdaEg1++WFqEgAAP9ZUWg for ; Tue, 01 Feb 2022 19:38:05 +0100 Received: from mailhub.math.univ-paris-diderot.fr (localhost [127.0.0.1]) by mailhub.math.univ-paris-diderot.fr (Postfix) with ESMTP id 473C810A597; Tue, 1 Feb 2022 19:38:05 +0100 (CET) X-Virus-Scanned: amavisd-new at math.univ-paris-diderot.fr X-Spam-Flag: NO X-Spam-Score: -1.637 X-Spam-Level: X-Spam-Status: No, score=-1.637 tagged_above=-10000 required=5 tests=[BAYES_00=-1.9, HEADER_FROM_DIFFERENT_DOMAINS=0.249, HTML_FONT_FACE_BAD=0.001, HTML_MESSAGE=0.001, MAILING_LIST_MULTI=-1, MISSING_HEADERS=1.021, T_SCC_BODY_TEXT_LINE=-0.01, URIBL_BLOCKED=0.001] autolearn=no autolearn_force=no Received: from mailhub.math.univ-paris-diderot.fr ([127.0.0.1]) by mailhub.math.univ-paris-diderot.fr (mailhub.math.univ-paris-diderot.fr [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id AhYHOKWp3fXZ; Tue, 1 Feb 2022 19:38:02 +0100 (CET) Received: from potemkin.univ-paris7.fr (potemkin.univ-paris7.fr [194.254.61.141]) by mailhub.math.univ-paris-diderot.fr (Postfix) with ESMTPS id CAF5510A592; Tue, 1 Feb 2022 19:38:02 +0100 (CET) Received: from mars.math-info.univ-paris5.fr (helios2.math-info.univ-paris5.fr [193.48.200.16]) by potemkin.univ-paris7.fr (8.14.4/8.14.4/relay2/82085) with ESMTP id 211Ic2mZ031728 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES256-SHA bits=256 verify=OK); Tue, 1 Feb 2022 19:38:02 +0100 Received: from mars.math-info.univ-paris5.fr (localhost [127.0.0.1]) by mars.math-info.univ-paris5.fr (8.14.4/jtpda-5.4) with ESMTP id 211Ic1WB019460 ; Tue, 1 Feb 2022 19:38:01 +0100 Received: (from sympa@localhost) by mars.math-info.univ-paris5.fr (8.14.4/8.14.3/Submit) id 211Ic0st019451; Tue, 1 Feb 2022 19:38:00 +0100 X-Authentication-Warning: mars.math-info.univ-paris5.fr: sympa set sender to diip-perimeter-owner@math-info.univ-paris5.fr using -f X-Sympa-To: diip-perimeter@math-info.univ-paris5.fr Received: from smtp-out02.parisdescartes.fr (mx1.parisdescartes.fr [193.51.86.61]) by mars.math-info.univ-paris5.fr (8.14.4/jtpda-5.4) with ESMTP id 211IaiJ5019280 for ; Tue, 1 Feb 2022 19:36:44 +0100 Received: from localhost (saroumane.univ-paris5.fr [192.168.253.9]) by mx1.parisdescartes.fr (Postfix) with ESMTP id D28CC241779; Tue, 1 Feb 2022 19:36:44 +0100 (CET) X-Virus-Scanned: amavisd-new at univ-paris5.fr Received: from mx1.parisdescartes.fr ([192.168.253.61]) by localhost (fourmilier.univ-paris5.fr [192.168.253.9]) (amavisd-new, port 10024) with ESMTP id 4eNixFel1OWm; Tue, 1 Feb 2022 19:36:40 +0100 (CET) Received: from goustan.fougeres.campus (goustan.fougeres.campus [172.22.6.50]) by smtp.parisdescartes.fr (Postfix) with ESMTPS id CBFAA241C8E; Tue, 1 Feb 2022 19:35:39 +0100 (CET) Received: from LEODAGAN.fougeres.campus (172.17.143.87) by goustan.fougeres.campus (172.22.6.50) with Microsoft SMTP Server (version=TLS1_2, cipher=TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256) id 15.1.2308.20; Tue, 1 Feb 2022 19:35:39 +0100 Received: from LEODAGAN.fougeres.campus ([fe80::a143:d7d6:e0b5:91c3]) by leodagan.fougeres.campus ([fe80::a143:d7d6:e0b5:91c3%10]) with mapi id 15.01.2308.020; Tue, 1 Feb 2022 19:35:39 +0100 From: Eva Lancelin CC: "diip@math-info.univ-paris5.fr" Thread-Topic: [reminder] diiP distinguished lecture, Feb2 Thread-Index: AQHYF5pueZYIfol7xUenz0MxYTJWHw== Date: Tue, 1 Feb 2022 18:35:39 +0000 Message-ID: <01a81dbc0a504cdca54e5569ad7ca10b@parisdescartes.fr> Accept-Language: fr-FR, en-US Content-Language: fr-FR X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [172.17.143.79] Content-Type: multipart/alternative; boundary="_000_01a81dbc0a504cdca54e5569ad7ca10bparisdescartesfr_" MIME-Version: 1.0 X-Validation-by: themis@mi.parisdescartes.fr Subject: [diip-perimeter] [reminder] diiP distinguished lecture, Feb2 Reply-To: Eva Lancelin X-Loop: diip-perimeter@math-info.univ-paris5.fr X-Sequence: 50 Errors-to: diip-perimeter-owner@math-info.univ-paris5.fr Precedence: list Precedence: bulk Sender: diip-perimeter-request@math-info.univ-paris5.fr X-no-archive: yes List-Id: List-Archive: List-Help: List-Owner: List-Post: List-Subscribe: List-Unsubscribe: X-Greylist: Sender succeeded STARTTLS authentication, not delayed by milter-greylist-4.2.7 (potemkin.univ-paris7.fr [194.254.61.141]); Tue, 01 Feb 2022 19:38:02 +0100 (CET) X-Miltered: at potemkin with ID 61F97E0A.003 by Joe's j-chkmail (http : // j-chkmail dot ensmp dot fr)! X-j-chkmail-Enveloppe: 61F97E0A.003 from helios2.math-info.univ-paris5.fr/helios2.math-info.univ-paris5.fr/null/mars.math-info.univ-paris5.fr/ X-j-chkmail-Score: MSGID : 61F97E0A.003 on potemkin.univ-paris7.fr : j-chkmail score : . : R=. U=. O=. B=0.000 -> S=0.000 X-j-chkmail-Status: Ham --_000_01a81dbc0a504cdca54e5569ad7ca10bparisdescartesfr_ Content-Type: text/plain; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable diiP Distinguished Lecture Finding Approximately Repeated Patterns in Time Series: The most Useful, an= d yet most Underutilized Primitive in Time Series Analytics Who: Prof. Eamonn Keogh (University of California Riverside) When: February 2nd, 4pm (Central European Time) Where: online (zoom) https://u-paris.zoom.us/j/88451939428?pwd=3DTWtyRHRPQXRvdjFIejFHRllxZDhyQT09 title: Finding Approximately Repeated Patterns in Time Series: The most Useful, an= d yet most Underutilized Primitive in Time Series Analytics 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 importanc= e. In this talk I shall argue that the simple primitive of time series moti= f discovery, the task of finding approximately repeated patterns with a dat= aset, 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 ta= sks, such as summarization, segmentation, classification, clustering and an= omaly detection. I will argue my case with examples of motif discovery in d= atasets as diverse as penguin behavior, cardiology, and astronomy. short bio: Eamonn Keogh is a distinguished professor and Ross Family Chair in the Depa= rtment of Computer Science and Engineering. He specializes in time series d= ata mining, finding patterns, regularities, and outliers in massive dataset= s. He developed some of the most commonly used definitions, algorithms and = data representations used in this area. These contributions include SAX, PA= A, Time Series Shapelets, Time Series Motifs, the LBkeogh lower bound, and = the Matrix Profile. These ideas have been used by thousands of academic, in= dustrial, and scientific researchers worldwide, including NASA=92s Jet Prop= ulsion Laboratory, which uses Keogh=92s ideas to find anomalies in observat= ions of the magnetosphere collected by the Cassini spacecraft in orbit arou= nd Saturn. In the week following this talk, he will be presented with the 2= 021 IEEE ICDM Research Contributions Award. logistics: Video, slides, and other material for this seminar will be posted on our we= bpage after the date of the seminar: Pour recevoir les annonces du diiP, vous pouvez vous inscrire =E0 notre mai= ling list en envoyant un mail =E0 diip@math-info.univ-paris5.fr avec "follow diiP" dans l'objet du mail. Warm regards, Eva Lancelin Project manager - Data Intelligence Institute of Paris Grands Moulins, bureau 878-C 5 rue Thomas Mann, 75013 Paris --_000_01a81dbc0a504cdca54e5569ad7ca10bparisdescartesfr_ Content-Type: text/html; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable

diiP Distinguished Lecture
Finding Approximately Repeated Patterns i= n Time Series: The most Useful, and yet most Underutilized Primitive in Time Series Analytics<= /div>
Who: Prof. Eamonn Keogh (University of California Riverside)
When: February 2nd, 4pm (Central European Time)
Where: online (zoom)

title:
Finding Approximately Repeated Patterns in Time Series: The most Useful, and yet m= ost Underutilized Primitive in Time Series Analytics
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 simple primitive of time series motif discovery, the task of finding approximatel= y repeated patterns with a dataset, is the most useful core operation in al= l 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 anomal= y detection. I will argue my case with examples of motif discovery in datas= ets as diverse as penguin behavior, cardiology, and astronomy.
short bio:
Eamonn Keogh is a distinguishe= d 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, algorit= hms and data representations used in this area. These contributions include= SAX, PAA, Time Series Shapelets, Time Series Motifs, the LBkeogh lower bou= nd, and the Matrix Profile. These ideas have been used by thousands of academic, industrial, and scientific = researchers worldwide, including NASA=92s Jet Propulsion Laboratory, which = uses Keogh=92s 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 presen= ted with the 2021 IEEE ICDM Research Contributions Award.
logistics:
Video, slides, and other material for this seminar will be pos= ted on our webpage after the date of the seminar: <https://u-paris.fr/diip/diip-semina= rs/>

Pour recevoir les annonces du diiP, vous pouvez vous inscrire =E0 notre mai= ling list en envoyant un mail =E0 diip@math-info.univ= -paris5.fr a= vec "follow diiP" dans l'objet du mail.<= /div>
Warm regards,


Eva Lancelin


Project manager - Data Intelligence Institute of Paris

Grands Moulins, bureau 878-C

5 rue Thomas Mann, 75013 Paris

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