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dc.contributor.authorNaseriparsa, Mehdi
dc.contributor.authorIslam, Md Saiful
dc.contributor.authorLiu, Chengfei
dc.contributor.authorChen, Lu
dc.date.accessioned2020-03-26T04:02:54Z
dc.date.available2020-03-26T04:02:54Z
dc.date.issued2019
dc.identifier.issn0169-023X
dc.identifier.doi10.1016/j.datak.2019.101758
dc.identifier.urihttp://hdl.handle.net/10072/392651
dc.description.abstractUsers are usually not familiar with the content and structure of the data when they explore the data source. However, to improve the exploration usability, they need some primary hints about the data source. These hints should represent the overall picture of the data source and include the trending issues that can be extracted from the query log. In this paper, we propose a two-phase interactive exploratory search framework for the clueless users that exploits the snippets for conducting the search on the XML data. In the first phase, we present the primary snippets that are generated from the keywords of the query log to start the exploration. To retrieve the primary snippets, we develop an A* search-based technique on the keyword space of the query log. To improve the performance of computations, we store the primary snippet computations in an index data structure to reuse it for the next steps. In the second phase, we exploit the co-occurring content of the snippets to generate more specific snippets with the user interaction. To expedite the performance, we design two pruning techniques called inter-snippet and intra-snippet pruning to stop unnecessary computations. Finally, we discuss a termination condition that checks the cardinality of the snippets to stop the interactive phase and present the final Top-l snippets to the user. Our experiments on real datasets verify the effectiveness and efficiency of the proposed framework.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofjournalData & Knowledge Engineering
dc.relation.ispartofvolume124
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchData Format
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0804
dc.subject.fieldofresearchcode0806
dc.subject.keywordsScience & Technology
dc.subject.keywordsComputer Science, Artificial Intelligence
dc.subject.keywordsComputer Science, Information Systems
dc.titleXSnippets: Exploring semi-structured data via snippets
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationNaseriparsa, M; Islam, MS; Liu, C; Chen, L, XSnippets: Exploring semi-structured data via snippets, Data & Knowledge Engineering, 2019, 124
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2020-03-26T03:58:41Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorIslam, Saiful


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