A Scalable Parallel Algorithm for Decision Support from Multidimensional Sequence Data

dc.contributor.advisor	Fazekas, Gábor
dc.contributor.author	Esmaeli, Mahdi
dc.contributor.department	Matematika- és számítástudományok doktori iskola	hu
dc.contributor.submitterdep	DE--TEK--Informatikai Kar --
dc.date.accessioned	2011-11-22T07:43:41Z
dc.date.available	2011-11-22T07:43:41Z
dc.date.defended	2011
dc.date.issued	2011
dc.description.abstract	In this work, we describe a multidimensional sequence model and then represent a parallel algorithm. We improve the primary parallel algorithm with two modification rules. Two approaches follow the level-wise approach and all participating processors or workers generate candidate sequences and count their supports independently. Our experiments show good load balancing and scalable and acceptable speedup over different processors and problem sizes and demonstrate that our approach can works efficiently in a real parallel computing environment.	hu_HU
dc.format.extent	102 p.	hu_HU
dc.identifier.uri	http://hdl.handle.net/2437/118958
dc.language.iso	en	hu_HU
dc.subject	Data Mining	hu_HU
dc.subject	Sequential Pattern Mining	hu_HU
dc.subject	Parallel Algorithm	hu_HU
dc.subject	Multidimensional Sequence Data	hu_HU
dc.subject.discipline	Matematika- és számítástudományok	hu
dc.subject.sciencefield	Természettudományok	hu
dc.title	A Scalable Parallel Algorithm for Decision Support from Multidimensional Sequence Data	hu_HU
dc.title.translated	-	hu_HU
dc.type	PhD, doktori értekezés	hu

Megjelenítve 1 - 4 (Összesen 4)