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dc.contributor.authorPiatov D
dc.contributor.authorHelmer S
dc.contributor.editorGertz M
dc.contributor.editorRenz M
dc.contributor.editorZhou X
dc.contributor.editorHoel E
dc.contributor.editorKu WS
dc.contributor.editorVoisard A
dc.contributor.editorZhang C
dc.contributor.editorChen H
dc.contributor.editorTang L
dc.contributor.editorHuang Y
dc.contributor.editorLu CT
dc.contributor.editorRavada S
dc.date.accessioned2018-07-30T14:05:06Z
dc.date.available2018-07-30T14:05:06Z
dc.date.issued2017
dc.identifier.isbn978-3-319-64366-3
dc.identifier.issn0302-9743
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-64367-0_7
dc.identifier.urihttp://hdl.handle.net/10863/5298
dc.description.abstractThe Timeline Index, which supports temporal joins, time travel, and temporal aggregation on constant intervals, has the potential of becoming a universal index for temporal database systems. Here we present a family of plane-sweeping algorithms that extend the set of operators supported by Timeline-Index-based databases to temporal aggregation on fixed intervals, such as a sliding windows or GROUP BY ROLLUP aggregation, and improve the existing algorithm for computing MIN/MAX temporal aggregates on constant intervals. Our method for selective aggregation relies on a new and very space-efficient data structure called MAX Skyline. In an empirical evaluation we show that our approach is superior to existing techniques, in some cases we improve the performance by orders of magnitude.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence;
dc.rights
dc.titleSweeping-Based Temporal Aggregationen_US
dc.typeBook chapteren_US
dc.date.updated2018-05-15T13:18:37Z
dc.publication.titleAdvances in Spatial and Temporal Databases: 15th International Symposium, SSTD 2017, Arlington, VA, USA, August 21 – 23, 2017, Proceedings
dc.language.isiEN-GB
dc.description.fulltextreserveden_US


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