Event Detection in Time Series
暫譯: 時間序列中的事件檢測

Ogasawara, Eduardo, Salles, Rebecca, Porto, Fabio

Event Detection in Time Series
  • 出版商: Springer
  • 出版日期: 2025-01-29
  • 售價: $1,870
  • 貴賓價: 9.5 折 $1,777
  • 語言: 英文
  • 頁數: 170
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031759400
  • ISBN-13: 9783031759406

    • 海外代購書籍(需單獨結帳)

商品描述

This book is dedicated to exploring and explaining time series event detection in databases. The focus is on events, which are pervasive in time series applications where significant changes in behavior are observed at specific points or time intervals. Event detection is a basic function in surveillance and monitoring systems and has been extensively explored over the years, but this book provides a unified overview of the major types of time series events with which researchers should be familiar: anomalies, change points, and motifs. The book starts with basic concepts of time series and presents a general taxonomy for event detection. This taxonomy includes (i) granularity of events (punctual, contextual, and collective), (ii) general strategies (regression, classification, clustering, model-based), (iii) methods (theory-driven, data-driven), (iv) machine learning processing (supervised, semi-supervised, unsupervised), and (v) data management (ETL process). This taxonomy is weaved throughout chapters dedicated to the specific event types: anomaly detection, change-point, and motif discovery. The book discusses state-of-the-art metric evaluations for event detection methods and also provides a dedicated chapter on online event detection, including the challenges and general approaches (static versus dynamic), including incremental and adaptive learning. This book will be of interested to graduate or undergraduate students of different fields with a basic introduction to data science or data analytics.

商品描述(中文翻譯)

本書專注於探索和解釋資料庫中的時間序列事件檢測。重點在於事件，這些事件在時間序列應用中普遍存在，特別是在特定時間點或時間間隔內觀察到行為的顯著變化。事件檢測是監控和監視系統中的基本功能，這一領域多年來已被廣泛研究，但本書提供了對研究人員應該熟悉的主要類型時間序列事件的統一概述：異常、變更點和模式。本書從時間序列的基本概念開始，並提出了一個事件檢測的一般分類法。這個分類法包括 (i) 事件的粒度（瞬時、上下文和集體），(ii) 一般策略（回歸、分類、聚類、基於模型），(iii) 方法（理論驅動、數據驅動），(iv) 機器學習處理（監督式、半監督式、非監督式），以及 (v) 數據管理（ETL過程）。這個分類法貫穿於專門針對特定事件類型的章節：異常檢測、變更點和模式發現。本書討論了事件檢測方法的最先進度量評估，並提供了一個專門的章節，介紹在線事件檢測，包括挑戰和一般方法（靜態與動態），包括增量學習和自適應學習。本書將對不同領域的研究生或本科生感興趣，特別是對數據科學或數據分析有基本介紹的讀者。

作者簡介

Eduardo Ogasawara has been a professor in the Department of Computer Science at the Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) since 2010. He holds a D.Sc. in Systems and Computer Engineering from COPPE/UFRJ. Between 2000 and 2007, he worked in the Information Technology (IT) sector, gaining extensive experience in workflows and project management. With a strong background in Data Science, he is currently focused on Data Mining and Time Series Analysis. He is a member of IEEE, ACM, and SBC. Throughout his career, he has authored numerous published articles and led projects funded by agencies such as CNPq and FAPERJ. Currently, he heads the Data Analytics Lab (DAL) at CEFET/RJ, where he continues to advance research in Data Science.

Rebecca Salles is a postdoctoral researcher at the Institut National de Recherche en Sciences et Technologies du Numérique (INRIA) in France. She holds a Ph.D. in Production Engineering and Systems (2023), an M.Sc. (with Honors, Best Dissertation award--SBBD 2021) (2019) and B.Sc. (summa cum laude, third-place award for Best Research--CSBC 2017) (2016) in Computer Science, and a technical degree in Industrial Informatics (2010) from the Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) in Brazil. As a data scientist, her research currently focuses on the topics of Data Mining, specializing in Time Series Analytics since 2014, including data pre-processing, predictive analysis, and event detection. She is an ACM member and has authored over 30 scientific products, including public frameworks and research papers published in well-known international conferences and scientific journals, also acting as a reviewer for DMKD, IEEE TKDE, and SBBD.

Fabio Porto is a Senior Researcher at the National Laboratory of Scientific Computing (LNCC) in Brazil. He is the founder of the Data Extreme Lab (DEXL) and the head of the AI Institute at LNCC. He holds an INRIA International Chair (2024-2028) at INRIA, France. Fabio earned his Ph.D. in Informatics from PUC-Rio in Brazil in 2001, with a research stay at INRIA (1999-2000), and completed a postdoc at the École Polytechnique Fédérale de Lausanne (EPFL) from 2004 to 2008. He has published more than 80 research papers in international conferences and scientific journals, including VLDB, SIGMOD, ICDE, and SBBD. He served as General Chair of VLDB 2018 and SBBD 2015. His main research interests include Data Management, Data-Driven AI, and Safety AI. He is a member of ACM and SBC.

Esther Pacitti is a professor of computer science at University of Montpellier. She is a senior researcher and co-head of the Zenith team at LIRMM, pursuing research in distributed data management. Previously, she was an assistant professor at University of Nantes (2002-2009) and a member of Atlas INRIA team. She obtained her "Habilitation à Diriger les Recherches" (HDR) degree in 2008 on the topic of data replication on different contexts (data warehouses, clusters and peer-to-peer systems). Since 2004 she has served or is serving as program committee member of major international conferences (VLDB, SIGMOD, CIKM, etc.) and has edited and co-authored several books. She has also published a significant amount of technical papers and journal papers in well-known international conferences and journals.

作者簡介(中文翻譯)

愛德華多·小笠原自2010年以來一直擔任里約熱內盧聯邦科技教育中心（CEFET/RJ）計算機科學系的教授。他擁有COPPE/UFRJ的系統與計算機工程博士學位。2000年至2007年間，他在資訊科技（IT）領域工作，積累了豐富的工作流程和專案管理經驗。憑藉在數據科學方面的堅實背景，他目前專注於數據挖掘和時間序列分析。他是IEEE、ACM和SBC的成員。在他的職業生涯中，他撰寫了多篇已發表的文章，並主導了由CNPq和FAPERJ等機構資助的專案。目前，他負責CEFET/RJ的數據分析實驗室（DAL），繼續推進數據科學的研究。

瑞貝卡·薩萊斯是法國國家數字科學與技術研究所（INRIA）的博士後研究員。她擁有生產工程與系統的博士學位（2023年）、碩士學位（榮譽，最佳論文獎--SBBD 2021）（2019年）和計算機科學的學士學位（summa cum laude，最佳研究第三名獎--CSBC 2017）（2016年），以及來自巴西里約熱內盧聯邦科技教育中心（CEFET/RJ）的工業資訊技術技術學位（2010年）。作為數據科學家，她目前的研究專注於數據挖掘，並自2014年以來專注於時間序列分析，包括數據預處理、預測分析和事件檢測。她是ACM的成員，已發表超過30項科學產品，包括公共框架和在知名國際會議及科學期刊上發表的研究論文，並擔任DMKD、IEEE TKDE和SBBD的審稿人。

法比奧·波爾托是巴西國家科學計算實驗室（LNCC）的高級研究員。他是數據極限實驗室（DEXL）的創始人，也是LNCC人工智慧研究所的負責人。他在法國INRIA擔任國際主席（2024-2028）。法比奧於2001年在巴西PUC-Rio獲得資訊學博士學位，並在INRIA進行了研究訪問（1999-2000），之後於2004年至2008年在洛桑聯邦理工學院（EPFL）完成博士後研究。他在國際會議和科學期刊上發表了超過80篇研究論文，包括VLDB、SIGMOD、ICDE和SBBD。他曾擔任VLDB 2018和SBBD 2015的總主席。他的主要研究興趣包括數據管理、數據驅動的人工智慧和安全人工智慧。他是ACM和SBC的成員。

艾絲特·帕基提是蒙彼利埃大學的計算機科學教授。她是LIRMM的高級研究員和Zenith團隊的共同負責人，專注於分佈式數據管理的研究。此前，她曾擔任南特大學的助理教授（2002-2009），並且是Atlas INRIA團隊的成員。她於2008年獲得了關於不同上下文（數據倉庫、集群和對等系統）數據複製的研究指導資格（HDR）學位。自2004年以來，她擔任或正在擔任多個國際會議（如VLDB、SIGMOD、CIKM等）的程序委員會成員，並編輯和共同撰寫了幾本書籍。她還在知名國際會議和期刊上發表了大量技術論文和期刊文章。

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