TEMPORAL GENERALIZATION WITH DOMAIN GENERALIZATION GRAPHS

Autor:	Dee Jay Randall, Howard J. Hamilton, Robert J. Hilderman
Rok vydání:	1999
Předmět:	Generalization Relational database business.industry Graph theory Temporal database Knowledge extraction Artificial Intelligence Formal specification Attribute domain Temporal logic Computer Vision and Pattern Recognition Artificial intelligence business Software Mathematics
Zdroj:	International Journal of Pattern Recognition and Artificial Intelligence. 13:195-217
ISSN:	1793-6381 0218-0014
DOI:	10.1142/s0218001499000124
Popis:	This paper addresses the problem of using domain generalization graphs to generalize temporal data extracted from relational databases. A domain generalization graph associated with an attribute defines a partial order which represents a set of generalization relations for the attribute. We propose formal specifications for domain generalization graphs associated with calendar (date and time) attributes. These graphs are reusable (i.e. can be used to generalize any calendar attributes), adaptable (i.e. can be extended or restricted as appropriate for particular applications), and transportable (i.e. can be used with any database containing a calendar attribute).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::07b5e98894181d6c37d2b6502b127f5c https://doi.org/10.1142/s0218001499000124 Zobrazit plný text záznamu