Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 140 pp. Englisch.

Bestandsnummer des Verk�ufers 9783031336928

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Bibliografische Details

Titel: Chronicles: Formalization of a Temporal Model
Verlag: Springer International Publishing, Springer Nature Switzerland Jul 2023
Erscheinungsjahr: 2023
Sprache: Englisch
ISBN-10: 3031336925
ISBN-13: 9783031336928
Einband: Taschenbuch
Zustand: Neu
Abmessungen: Breite: 235 mm, H�he: 155 mm, Tiefe: 8 mm
Gewicht: 224 Gramm

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Inhaltsangabe

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.

�ber die Autorin bzw. den Autor

Philippe Besnard is Senior researcher at CNRS/IRIT (Toulouse, France). His research interests are about knowledge representation through logic. In addition to work on computational argumentation, he mainly focused on non-classical logics to formalize non-monotonic reasoning and paraconsistent reasoning.
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)

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