Event Detection in Time Series (Synthesis Lectures on Data Management) - Hardcover

Ogasawara, Eduardo; Salles, Rebecca; Porto, Fabio; Pacitti, Esther

Hardcover

ISBN 10: 3031759400 ISBN 13: 9783031759406

Publisher: Springer, 2025

Synopsis

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.

About this title

About the Author

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.

From the Back Cover

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.

In addition, this book: 

• Develops a taxonomy covering granularity of events, general strategies, methods, machine learning processing, and data management
• Provides a unified overview of the major types of time series events: anomalies, change points, and motifs
• Discusses state-of-the-art metric evaluations for event detection methods as well as online event detection

Edition details

• Publisher: Springer
• Publication date: 2025
• Language: English
• ISBN 10: 3031759400
• ISBN 13: 9783031759406
• Binding: Hardcover
• Number of pages: 182