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Conference Paper: On-line preferential nearest neighbor browsing in large attributed graphs

TitleOn-line preferential nearest neighbor browsing in large attributed graphs
Authors
KeywordsAttributed graphs
Data graph
Nearest neighbors
New approaches
Preferred features
Issue Date2010
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2010, v. 6193 LNCS, p. 2-19 How to Cite?
AbstractGiven a large weighted directed graph where nodes are associated with attributes and edges are weighted, we study a new problem, called preferential nearest neighbors (NN) browsing, in this paper. In such browsing, a user may provide one or more source nodes and some keywords to retrieve the nearest neighbors of those source nodes that contain the given keywords. For example, when a tourist has a plan to visit several places (source nodes), he/she would like to search hotels with some preferred features (e.g., Internet and swimming pools). It is highly desirable to recommend a list of near hotels with those preferred features, in order of the road network distance to the places (source nodes) the tourist wants to visit. The existing approach by graph traversal at querying time requires long query processing time, and the approach by maintenance of the pre-computed all-pairs shortest distances requires huge storage space on disk. In this paper, we propose new approaches to support on-line preferential NN browsing. The data graphs we are dealing with are weighted directed graphs where nodes are associated with attributes, and the distances between nodes to be found are the exact distances in the graph. We focus ourselves on two-step approaches. In the first step, we identify a number of reference nodes (also called centers) which exist alone on some shortest paths between a source node and a preferential NN node that contains the user-given keywords. In the second step, we find the preferential NN nodes within a certain distance to the source nodes via the relevant reference nodes, using an index that supports both textural (attributes) and and the distance. Our approach tightly integrates NN search with the preference search, which is confirmed to be efficient and effective to find any preferential NN nodes. © 2010 Springer-Verlag Berlin Heidelberg.
DescriptionLecture Notes in Computer Science, 2010, v. 6193, p. 2-19
Persistent Identifierhttp://hdl.handle.net/10722/129567
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorCheng, Jen_HK
dc.contributor.authorYu, JXen_HK
dc.contributor.authorCheng, RCKen_HK
dc.date.accessioned2010-12-23T08:39:20Z-
dc.date.available2010-12-23T08:39:20Z-
dc.date.issued2010en_HK
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2010, v. 6193 LNCS, p. 2-19en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129567-
dc.descriptionLecture Notes in Computer Science, 2010, v. 6193, p. 2-19-
dc.description.abstractGiven a large weighted directed graph where nodes are associated with attributes and edges are weighted, we study a new problem, called preferential nearest neighbors (NN) browsing, in this paper. In such browsing, a user may provide one or more source nodes and some keywords to retrieve the nearest neighbors of those source nodes that contain the given keywords. For example, when a tourist has a plan to visit several places (source nodes), he/she would like to search hotels with some preferred features (e.g., Internet and swimming pools). It is highly desirable to recommend a list of near hotels with those preferred features, in order of the road network distance to the places (source nodes) the tourist wants to visit. The existing approach by graph traversal at querying time requires long query processing time, and the approach by maintenance of the pre-computed all-pairs shortest distances requires huge storage space on disk. In this paper, we propose new approaches to support on-line preferential NN browsing. The data graphs we are dealing with are weighted directed graphs where nodes are associated with attributes, and the distances between nodes to be found are the exact distances in the graph. We focus ourselves on two-step approaches. In the first step, we identify a number of reference nodes (also called centers) which exist alone on some shortest paths between a source node and a preferential NN node that contains the user-given keywords. In the second step, we find the preferential NN nodes within a certain distance to the source nodes via the relevant reference nodes, using an index that supports both textural (attributes) and and the distance. Our approach tightly integrates NN search with the preference search, which is confirmed to be efficient and effective to find any preferential NN nodes. © 2010 Springer-Verlag Berlin Heidelberg.en_HK
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectAttributed graphs-
dc.subjectData graph-
dc.subjectNearest neighbors-
dc.subjectNew approaches-
dc.subjectPreferred features-
dc.titleOn-line preferential nearest neighbor browsing in large attributed graphsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0302-9743&volume=6193&spage=2&epage=19&date=2010&atitle=On-line+preferential+nearest+neighbor+browsing+in+large+attributed+graphs-
dc.identifier.emailCheng, RCK:ckcheng@cs.hku.hken_HK
dc.identifier.authorityCheng, RCK=rp00074en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-14589-6_2en_HK
dc.identifier.scopuseid_2-s2.0-77956138379en_HK
dc.identifier.hkuros176459en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77956138379&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume6193 LNCSen_HK
dc.identifier.spage2en_HK
dc.identifier.epage19en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridCheng, J=23391876200en_HK
dc.identifier.scopusauthoridYu, JX=7405530530en_HK
dc.identifier.scopusauthoridCheng, RCK=7201955416en_HK

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