Indexing objects moving on fixed networks

Frentzos, E

dc.contributor.author	Frentzos, E	en
dc.date.accessioned	2014-03-01T01:19:03Z
dc.date.available	2014-03-01T01:19:03Z
dc.date.issued	2003	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15341
dc.subject	Access Method	en
dc.subject	Indexation	en
dc.subject	Moving Object	en
dc.subject	Real World Application	en
dc.subject	Road Network	en
dc.subject	Spatial Data	en
dc.subject	Spatiotemporal Databases	en
dc.subject	1 dimensional	en
dc.subject	2 dimensional	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.title	Indexing objects moving on fixed networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/978-3-540-45072-6_17	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-45072-6_17	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	The development of a spatiotemporal access method suitable for objects moving on fixed networks is a very attractive challenge due to the great number of real-world spatiotemporal database applications and fleet management systems dealing with this type of objects. In this work, a new indexing technique, named Fixed Network R-Tree (FNR-Tree), is proposed for objects constrained to move on fixed networks in 2-dimensional space. The general idea that describes the FNR-Tree is a forest of 1-dimensional (ID) R-Trees on top of a 2-dimensional (2D) R-Tree. The 2D R-Tree is used to index the spatial data of the network (e.g. roads consisting of line segments), while the ID R-Trees are used to index the time interval of each object's movement inside a given link of the network. The performance study, comparing this novel access method with the traditional R-Tree under various datasets and queries, shows that the FNR-Tree outperforms the R-Tree in most cases. © Springer-Verlag Berlin Heidelberg 2003.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/978-3-540-45072-6_17	en
dc.identifier.isi	ISI:000185178500017	en
dc.identifier.volume	2750	en
dc.identifier.spage	289	en
dc.identifier.epage	305	en