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dc.contributor.authorNaeem, M
dc.contributor.authorDobbie, G
dc.contributor.authorWeber, G
dc.contributor.editorDayal, U
dc.contributor.editorCastellanos, M
dc.contributor.editorMiller, RJ
dc.date.accessioned2012-04-26T01:42:32Z
dc.date.available2012-04-26T01:42:32Z
dc.date.copyright2009
dc.date.issued2012-04-26
dc.identifier.citationWorkshop on Enabling Real-Time for Business Intelligence, France, pages 155 - 170
dc.identifier.isbn978-3-642-14558-2
dc.identifier.urihttp://hdl.handle.net/10292/4054
dc.description.abstractOne problem encountered in real-time data integration is the join of a continuous incoming data stream with a disk-based relation. In this paper we investigate a stream-based join algorithm, called mesh join (MESHJOIN), and focus on a critical component in the algorithm, called the disk-buffer. In MESHJOIN the size of disk-buffer varies with a change in total memory budget and tuning is required to get the maximum service rate within limited available memory. Until now there was little data on the position of the optimum value depending on the memory size, and no performance comparison has been carried out between the optimum and reasonable default sizes for the disk-buffer. To avoid tuning, we propose a reasonable default value for the disk-buffer size with a small and acceptable performance loss. The experimental results validate our arguments.
dc.publisherSpringer
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. The final publication is available at www.springerlink.com
dc.subjectETL for real-time data warehouse
dc.subjectETL optimization
dc.subjectTuning and management of the real-time data warehouse
dc.subjectPerformance and scalability
dc.subjectStream-based join
dc.titleComparing global optimization and default settings of stream-based joins
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1007/978-3-642-14559-9_10


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