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008			230725s2023 sz | o |||| 0|eng d
020			_a9783031336935
024	7		_a10.1007/978-3-031-33693-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D343 _b2023 EB
100	1		_aGuyet, Thomas _eautor _0(orcid)0000-0002-4909-5843 _1https://orcid.org/0000-0002-4909-5843 _4http://id.loc.gov/vocabulary/relators/aut _9689535
245	1	0	_aChronicles : _bFormalization of a Temporal Model _cby Thomas Guyet, Philippe Besnard
250			_a1st ed 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aSpringerBriefs in Computer Science _x2191-5776
520			_aThis 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. .
988			_aSpringer_Computer_2023
650		7	_2embne _9162648 _aData mining
700	1		_9689536 _aBesnard, Philippe _d1958- _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-33693-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _eb _zSI