dc.contributor.author | Alaqta, I | |
dc.contributor.author | Wang, J | |
dc.contributor.author | Awrangjeb, M | |
dc.date.accessioned | 2020-03-02T06:36:13Z | |
dc.date.available | 2020-03-02T06:36:13Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 9783030352301 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.doi | 10.1007/978-3-030-35231-8_20 | |
dc.identifier.uri | http://hdl.handle.net/10072/392029 | |
dc.description.abstract | Community search is defined as finding query-based communities within simple graphs. One of the most crucial community models is minimum degree subgraph in which each vertex has at least k neighbours. Due to the rapid development of location-based devices; however, simple graphs are unable to handle Location-Based Social Networks LBSN personal information such as interests and spatial locations. Hence, this paper aims to construct a Precomputed Hybrid Index Architecture (PHIA) for the sake of enhancing simple graphs to store and retrieve information of LBSN users. This method consists of two stages; the first is precomputing, and the second is index construction. Numerical testing showed that our hybrid index approach is reasonable because of its flexibility to combine different dimensions by adapting the wide used community model | |
dc.description.peerreviewed | Yes | |
dc.publisher | Springer | |
dc.relation.ispartofconferencename | 15th International Conference on Advanced Data Mining and Applications (ADMA 2019) | |
dc.relation.ispartofconferencetitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.relation.ispartofdatefrom | 2019-11-21 | |
dc.relation.ispartofdateto | 2019-11-23 | |
dc.relation.ispartoflocation | Dalian, China | |
dc.relation.ispartofpagefrom | 277 | |
dc.relation.ispartofpageto | 287 | |
dc.relation.ispartofseries | Advanced Data Mining and Applications | |
dc.relation.ispartofvolume | 11888 | |
dc.subject.fieldofresearch | Artificial intelligence | |
dc.subject.fieldofresearchcode | 4602 | |
dc.title | Precomputing Hybrid Index Architecture for Flexible Community Search over Location-Based Social Networks | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Alaqta, I; Wang, J; Awrangjeb, M, Precomputing Hybrid Index Architecture for Flexible Community Search over Location-Based Social Networks, Lecture Notes in Computer Science, 2019, 11888, pp. 277-287 | |
dc.date.updated | 2020-02-28T05:56:38Z | |
dc.description.version | Accepted Manuscript (AM) | |
gro.rights.copyright | © Springer Nature Switzerland AG 2019. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Awrangjeb, Mohammad | |