Overview
- Delivers a comprehensive review of methods in motif discovery along with the research gaps in this domain
- Covers mathematical theories as invariant and wavelet theory
- Provides new directions for the domain of image processing
Part of the book series: Technologien für die intelligente Automation (TIA, volume 15)
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Table of contents (7 chapters)
Keywords
About this book
This book includes a novel motif discovery for time series, KITE (ill-Known motIf discovery in Time sEries data), to identify ill-known motifs transformed by affine mappings such as translation, uniform scaling, reflection, stretch, and squeeze mappings. Additionally, such motifs may be covered with noise or have variable lengths. Besides KITE’s contribution to motif discovery, new avenues for the signal and image processing domains are explored and created. The core of KITE is an invariant representation method called Analytic Complex Quad Tree Wavelet Packet transform (ACQTWP). This wavelet transform applies to motif discovery as well as to several signal and image processing tasks. The efficiency of KITE is demonstrated with data sets from various domains and compared with state-of-the-art algorithms, where KITE yields the best outcomes.
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Authors and Affiliations
About the author
Sahar Deppe studied Electrical Engineering and Information Technology at Halmstad University (Halmstad, Sweden) and the OWL University of Applied Sciences and Arts (Lemgo, Germany), where she received her Master degree. From 2013 to 2020 she was employed at the Institute Industrial IT (inIT) as a research associate and during this time she completed her doctorate (Dr. rer. nat.) in cooperative graduation with Paderborn University. Since 2020 she is employed at the Fraunhofer Institute IOSB-INA as a research associate with project management responsibilities.
In her dissertation, she proposed a novel method to detect motifs in time series data based on mathematical theories suited to represent and handle ill-known motifs such as invariant theory and theories in signal processing such as wavelet theory. Her research interests include but are not limited to the area of motif discovery and time series analysis, pattern recognition, and machine learning. She has published and presented her research at numerous conferences and journals such as IEEE, IARIA, PESARO where she got the best paper award for her research in motif discovery in image data.
Bibliographic Information
Book Title: Discovery of Ill–Known Motifs in Time Series Data
Authors: Sahar Deppe
Series Title: Technologien für die intelligente Automation
DOI: https://doi.org/10.1007/978-3-662-64215-3
Publisher: Springer Vieweg Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature 2022
Softcover ISBN: 978-3-662-64214-6Published: 02 October 2021
eBook ISBN: 978-3-662-64215-3Published: 01 October 2021
Series ISSN: 2522-8579
Series E-ISSN: 2522-8587
Edition Number: 1
Number of Pages: XIV, 205
Number of Illustrations: 18 b/w illustrations, 30 illustrations in colour
Topics: Statistical Theory and Methods, Signal, Image and Speech Processing, Computer Imaging, Vision, Pattern Recognition and Graphics