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Table of contents (6 chapters)
Keywords
- Mining Temporal Data
- Data mining
- Spatial data
- Pattern Recognition
- Spatial Science
- temporal model
- temporal sequences
- temporal network
- temporal abstraction
- symbolic time series
- event sequences
- activity recognition
- pattern mining
- temporal patterns
- pattern occurrences
- pattern enumeration
- formal concept analysis
- lattice
- order theory
- pattern structures
About this book
This book is intended as an introduction to a versatile model for temporal data. It exhibits an original lattice structure on the space of chronicles and proposes new counting approach for multiple occurrences of chronicle occurrences. This book also proposes a new approach for frequent temporal pattern mining using pattern structures. This book was initiated by the work of Ch. Dousson in the 1990’s. At that time, the prominent format was Temporal Constraint Networks for which the article by Richter, Meiri and Pearl is seminal.
Chronicles do not conflict with temporal constraint networks, they are closely related. Not only do they share a similar graphical representation, they also have in common a notion of constraints in the timed succession of events. However, chronicles are definitely oriented towards fairly specific tasks in handling temporal data, by making explicit certain aspects of temporal data such as repetitions of an event. The notion of chronicle has been applied both for situation recognition and temporal sequence abstraction. The first challenge benefits from the simple but expressive formalism to specify temporal behavior to match in a temporal sequence. The second challenge aims to abstract a collection of sequences by chronicles with the objective to extract characteristic behaviors.
This book targets researchers and students in computer science (from logic to data science). Engineers who would like to develop algorithms based on temporal models will also find this book useful.
Authors and Affiliations
About the authors
Thomas Guyet is researcher at Inria (Lyon, France). He received a PhD thesis in Computer Science in 2007 from National Polytechnic Institute of Grenoble (France). From 2007 to 2021, he was assistant professor at Institut Agro and was working in the IRISA laboratory in Rennes (France). In 2020, he joined Inria in Lyon as full researcher. His research domain is mainly spatial and temporal data analysis using various computer science paradigms ranging from artificial intelligence domain (logic programming, semantic web, machine learning) to algorithmic (sequential pattern mining, time series analysis). He applies his research mainly to lifescience challenges (medicine, environment and biology)
Bibliographic Information
Book Title: Chronicles: Formalization of a Temporal Model
Authors: Thomas Guyet, Philippe Besnard
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-031-33693-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Softcover ISBN: 978-3-031-33692-8Published: 26 July 2023
eBook ISBN: 978-3-031-33693-5Published: 25 July 2023
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XVI, 121
Number of Illustrations: 33 b/w illustrations, 10 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Pattern Recognition, Regional/Spatial Science