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020 _a9783031336935
_9978-3-031-33693-5
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_223
100 1 _aGuyet, Thomas.
_eauthor.
_0(orcid)0000-0002-4909-5843
_1https://orcid.org/0000-0002-4909-5843
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aChronicles: Formalization of a Temporal Model
_h[electronic resource] /
_cby Thomas Guyet, Philippe Besnard.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXVI, 121 p. 43 illus., 10 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
500 _aAcceso multiusuario
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. .
541 _fUABC ;
_cPerpetuidad
650 0 _aData mining.
650 0 _aPattern recognition systems.
650 0 _aSpace in economics.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aAutomated Pattern Recognition.
650 2 4 _aSpatial Economics.
700 1 _aBesnard, Philippe.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031336928
776 0 8 _iPrinted edition:
_z9783031336942
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-33693-5
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