Show simple item record

Quantitative analysis of networked environments to improve performance of information systems
dc.contributor.advisorPokorný, Jaroslav
dc.creatorPetříček, Václav
dc.date.accessioned2018-11-30T14:09:40Z
dc.date.available2018-11-30T14:09:40Z
dc.date.issued2007
dc.identifier.urihttp://hdl.handle.net/20.500.11956/13732
dc.description.abstractIn this thesis we encounter networks in three contexts i) as the citation networks between documents in citation databases CiteSeer and DBLP, ii) as the structure of e-government websites that is navigated by users and iii) as the social network of users of a photo-sharing site Flickr and a social networking site Yahoo!360. We study the properties of networks present in real datasets, what are the effects of their structure and how this structure can be exploited. We analyze the citation networks between computer science publications and compare them to those described in Physics community. We also demonstrate the bias of citation databases collected autonomously and present mathematical models of this bias. We then analyze the link structure of three websites extracted by exhaustive crawls. We perform a user study with 134 participants on these websites in an lab. We discuss the structure of the link networks and the performance of subjects in locating information on these websites. We finally exploit the knowledge of users' social network to provide higher quality recommendations than current collaborative filtering techniques and demonstrate the performance benefit on two real datasets.en_US
dc.languageEnglishcs_CZ
dc.language.isoen_US
dc.publisherUniverzita Karlova, Matematicko-fyzikální fakultacs_CZ
dc.titleQuantitative analysis of networked environments to improve performance of information systemsen_US
dc.typedizertační prácecs_CZ
dcterms.created2007
dcterms.dateAccepted2007-09-24
dc.description.departmentKatedra softwarového inženýrstvícs_CZ
dc.description.departmentDepartment of Software Engineeringen_US
dc.description.facultyFaculty of Mathematics and Physicsen_US
dc.description.facultyMatematicko-fyzikální fakultacs_CZ
dc.identifier.repId42985
dc.title.translatedQuantitative analysis of networked environments to improve performance of information systemscs_CZ
dc.contributor.refereeCox, Ingemar J.
dc.contributor.refereeSnášel, Václav
dc.identifier.aleph000855718
thesis.degree.namePh.D.
thesis.degree.leveldoktorskécs_CZ
thesis.degree.disciplineSoftwarové systémycs_CZ
thesis.degree.disciplineSoftware Systemsen_US
thesis.degree.programInformaticsen_US
thesis.degree.programInformatikacs_CZ
uk.thesis.typedizertační prácecs_CZ
uk.taxonomy.organization-csMatematicko-fyzikální fakulta::Katedra softwarového inženýrstvícs_CZ
uk.taxonomy.organization-enFaculty of Mathematics and Physics::Department of Software Engineeringen_US
uk.faculty-name.csMatematicko-fyzikální fakultacs_CZ
uk.faculty-name.enFaculty of Mathematics and Physicsen_US
uk.faculty-abbr.csMFFcs_CZ
uk.degree-discipline.csSoftwarové systémycs_CZ
uk.degree-discipline.enSoftware Systemsen_US
uk.degree-program.csInformatikacs_CZ
uk.degree-program.enInformaticsen_US
thesis.grade.csProspěl/acs_CZ
thesis.grade.enPassen_US
uk.abstract.enIn this thesis we encounter networks in three contexts i) as the citation networks between documents in citation databases CiteSeer and DBLP, ii) as the structure of e-government websites that is navigated by users and iii) as the social network of users of a photo-sharing site Flickr and a social networking site Yahoo!360. We study the properties of networks present in real datasets, what are the effects of their structure and how this structure can be exploited. We analyze the citation networks between computer science publications and compare them to those described in Physics community. We also demonstrate the bias of citation databases collected autonomously and present mathematical models of this bias. We then analyze the link structure of three websites extracted by exhaustive crawls. We perform a user study with 134 participants on these websites in an lab. We discuss the structure of the link networks and the performance of subjects in locating information on these websites. We finally exploit the knowledge of users' social network to provide higher quality recommendations than current collaborative filtering techniques and demonstrate the performance benefit on two real datasets.en_US
uk.file-availabilityV
uk.publication.placePrahacs_CZ
uk.grantorUniverzita Karlova, Matematicko-fyzikální fakulta, Katedra softwarového inženýrstvícs_CZ
thesis.grade.codeP


Files in this item

Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record


© 2017 Univerzita Karlova, Ústřední knihovna, Ovocný trh 560/5, 116 36 Praha 1; email: admin-repozitar [at] cuni.cz

Za dodržení všech ustanovení autorského zákona jsou zodpovědné jednotlivé složky Univerzity Karlovy. / Each constituent part of Charles University is responsible for adherence to all provisions of the copyright law.

Upozornění / Notice: Získané informace nemohou být použity k výdělečným účelům nebo vydávány za studijní, vědeckou nebo jinou tvůrčí činnost jiné osoby než autora. / Any retrieved information shall not be used for any commercial purposes or claimed as results of studying, scientific or any other creative activities of any person other than the author.

DSpace software copyright © 2002-2015  DuraSpace
Theme by 
@mire NV