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dc.contributor.author Nam, Yoon-Min ko
dc.contributor.author Han, Donghyoung ko
dc.contributor.author Kim M.-S.K. ko
dc.date.accessioned 2021-01-29T07:30:59Z -
dc.date.available 2021-01-29T07:30:59Z -
dc.date.created 2020-06-30 -
dc.date.issued 2020-06-14 -
dc.identifier.citation 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020, pp.2055 - 2070 -
dc.identifier.isbn 9781450367356 -
dc.identifier.issn 0730-8078 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/12905 -
dc.description.abstract The concept of OLAP query processing is now being widely adopted in various applications. The number of complex queries containing the joins between non-unique keys (called FK-FK joins) increases in those applications. However, the existing in-memory OLAP systems tend not to handle such complex queries efficiently since they generate a large amount of intermediate results or incur a huge amount of probe cost. In this paper, we propose an effective query planning method for complex OLAP queries. It generates a query plan containing n-ary join operators based on a cost model. The plan does not generate intermediate results for processing FK-FK joins and significantly reduces the probe cost. We also propose an efficient processing method for n-ary join operators. We implement the prototype system SPRINTER by integrating our proposed methods into an open-source in-memory OLAP system. Through experiments using the TPC-DS benchmark, we have shown that SPRINTER outperforms the state-of-the-art OLAP systems for complex queries. © 2020 Association for Computing Machinery. -
dc.language English -
dc.publisher Association for Computing Machinery -
dc.title SPRINTER: A Fast n-ary Join Query Processing Method for Complex OLAP Queries -
dc.type Conference -
dc.identifier.doi 10.1145/3318464.3380565 -
dc.identifier.scopusid 2-s2.0-85086257165 -
dc.type.local Article(Overseas) -
dc.type.rims CONF -
dc.description.journalClass 1 -
dc.contributor.localauthor Kim M.-S.K. -
dc.identifier.citationStartPage 2055 -
dc.identifier.citationEndPage 2070 -
dc.identifier.citationTitle 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 -
dc.identifier.conferencecountry US -
dc.identifier.conferencelocation Portland -

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