Constrained shortest path computation

Terrovitis, M; Bakiras, S; Papadias, D; Mouratidis, K

dc.contributor.author	Terrovitis, M	en
dc.contributor.author	Bakiras, S	en
dc.contributor.author	Papadias, D	en
dc.contributor.author	Mouratidis, K	en
dc.date.accessioned	2014-03-01T02:43:11Z
dc.date.available	2014-03-01T02:43:11Z
dc.date.issued	2005	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31279
dc.subject	Data Partitioning	en
dc.subject	Exact Algorithm	en
dc.subject	Indexation	en
dc.subject	Optimal Path	en
dc.subject	Search Space	en
dc.subject	Spatial Database	en
dc.subject	Shortest Path	en
dc.subject	Shortest Path Problem	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Algorithms	en
dc.subject.other	Constraint theory	en
dc.subject.other	Database systems	en
dc.subject.other	Data-partitioning method	en
dc.subject.other	Eucledean distance method	en
dc.subject.other	Spatial databases	en
dc.subject.other	Sub-optimal method	en
dc.subject.other	Computational methods	en
dc.title	Constrained shortest path computation	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11535331_11	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11535331_11	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	This paper proposes and solves α-autonomy and κ-stops shortest path problems in large spatial databases. Given a source s and a destination d, an α-autonomy query retrieves a sequence of data points connecting s and d, such that the distance between any two consecutive points in the path is not greater than α. A κ-stops query retrieves a sequence that contains exactly κ intermediate data points. In both cases our aim is to compute the shortest path subject to these constraints. Assuming that the dataset is indexed by a data-partitioning method, the proposed techniques initially compute a sub-optimal path by utilizing the Euclidean distance information provided by the index. The length of the retrieved path is used to prune the search space, filtering out large parts of the input dataset. In a final step, the optimal (α-autonomy or κ-stops) path is computed (using only the non-eliminated data points) by an exact algorithm. We discuss several processing methods for both problems, and evaluate their efficiency through extensive experiments. © Springer-Verlag Berlin Heidelberg 2005.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11535331_11	en
dc.identifier.isi	ISI:000231416800011	en
dc.identifier.volume	3633	en
dc.identifier.spage	181	en
dc.identifier.epage	199	en