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Scaling the ConceptCloud browser to large semi-structured data sets

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dc.contributor.author Berndt, Joshua
dc.contributor.author Fischer, Bernd
dc.contributor.author Britz, Arina
dc.date.accessioned 2019-03-07T09:47:05Z
dc.date.available 2019-03-07T09:47:05Z
dc.date.issued 2018-10
dc.identifier.citation Berndt, J., Fischer, B. and Britz, A. 2018. Scaling the ConceptCloud browser to large semi-structured data sets. Proceedings 14th African Conference on Research in Computer Science and Applied Mathematics, Stellenbosch, South Africa, 14-16 October 2018, pp. 276-283 en_US
dc.identifier.uri https://hal.inria.fr/hal-01881376/file/CARI2018_Proceedings.pdf
dc.identifier.uri https://hal.inria.fr/hal-01881376
dc.identifier.uri http://hdl.handle.net/10204/10747
dc.description Paper presented at the 14th African Conference on Research in Computer Science and Applied Mathematics, Stellenbosch, South Africa, 14-16 October 2018 en_US
dc.description.abstract Semi-structured data sets such as product reviews or event log data are simultaneously becoming more widely used and growing ever larger. This paper describes ConceptCloud, a flexible interactive browser for semi-structured datasets, with a focus on the recent trend of implementing server-based architectures to accommodate ever growing datasets. ConceptCloud makes use of an intuitive tag cloud visualization viewer in combination with an underlying concept lattice to provide a formal structure for navigation through datasets without prior knowledge of the structure of the data or compromising scalability. This is achieved by implementing architectural changes to increase the system’s resource efficiency. en_US
dc.language.iso en en_US
dc.publisher HAL en_US
dc.relation.ispartofseries Worklist;22131
dc.subject Concept lattice en_US
dc.subject Client-server architecture en_US
dc.subject Semi-structured data en_US
dc.subject Tag cloud en_US
dc.title Scaling the ConceptCloud browser to large semi-structured data sets en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Berndt, J., Fischer, B., & Britz, A. (2018). Scaling the ConceptCloud browser to large semi-structured data sets. HAL. http://hdl.handle.net/10204/10747 en_ZA
dc.identifier.chicagocitation Berndt, Joshua, Bernd Fischer, and Arina Britz. "Scaling the ConceptCloud browser to large semi-structured data sets." (2018): http://hdl.handle.net/10204/10747 en_ZA
dc.identifier.vancouvercitation Berndt J, Fischer B, Britz A, Scaling the ConceptCloud browser to large semi-structured data sets; HAL; 2018. http://hdl.handle.net/10204/10747 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Berndt, Joshua AU - Fischer, Bernd AU - Britz, Arina AB - Semi-structured data sets such as product reviews or event log data are simultaneously becoming more widely used and growing ever larger. This paper describes ConceptCloud, a flexible interactive browser for semi-structured datasets, with a focus on the recent trend of implementing server-based architectures to accommodate ever growing datasets. ConceptCloud makes use of an intuitive tag cloud visualization viewer in combination with an underlying concept lattice to provide a formal structure for navigation through datasets without prior knowledge of the structure of the data or compromising scalability. This is achieved by implementing architectural changes to increase the system’s resource efficiency. DA - 2018-10 DB - ResearchSpace DP - CSIR KW - Concept lattice KW - Client-server architecture KW - Semi-structured data KW - Tag cloud LK - https://researchspace.csir.co.za PY - 2018 T1 - Scaling the ConceptCloud browser to large semi-structured data sets TI - Scaling the ConceptCloud browser to large semi-structured data sets UR - http://hdl.handle.net/10204/10747 ER - en_ZA


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