000219207 001__ 219207
000219207 005__ 20181114202629.0
000219207 037__ $$aCONF
000219207 245__ $$aSquall: Scalable Real-time Analytics
000219207 269__ $$a2016
000219207 260__ $$bVLDB Endowment Inc.$$c2016
000219207 336__ $$aConference Papers
000219207 520__ $$aSquall is a scalable online query engine that runs complex analytics in a cluster using skew-resilient, adaptive operators. Squall builds on state-of-the-art partitioning schemes and local algorithms, including some of our own. This paper presents the overview of Squall, including some novel join operators. The paper also presents lessons learned over the five years of working on this system, and outlines the plan for the proposed system demonstration.
000219207 6531_ $$aonline multi-way joins
000219207 6531_ $$askew-resilience
000219207 6531_ $$ascalability
000219207 700__ $$0245575$$aVitorovic, Aleksandar$$g199788
000219207 700__ $$aEl Seidy, Mohammed
000219207 700__ $$aGuliyev, Khayyam Mubariz Oglu
000219207 700__ $$aVu, Minh Khue
000219207 700__ $$aEspino Timón, Daniel
000219207 700__ $$aDashti, Mohammad
000219207 700__ $$0246551$$aKlonatos, Ioannis$$g215191
000219207 700__ $$0244689$$aKoch, Christoph$$g205917
000219207 7112_ $$a42nd International Conference on Very Large Data Bases$$cNew Delhi, India$$dSeptember 5-9, 2016
000219207 773__ $$j9$$k13$$tProceedings of the VLDB Endowment
000219207 8564_ $$uhttps://infoscience.epfl.ch/record/217286?ln=en$$xPUBLIC$$zURL
000219207 8564_ $$s1205929$$uhttps://infoscience.epfl.ch/record/219207/files/Squall_Scalable_Realtime_Analytics.pdf$$yn/a$$zn/a
000219207 909C0 $$0252342$$pDATA$$xU12327
000219207 909CO $$ooai:infoscience.tind.io:219207$$pconf$$pIC$$qGLOBAL_SET
000219207 917Z8 $$x199788
000219207 937__ $$aEPFL-CONF-219207
000219207 973__ $$aEPFL$$rREVIEWED$$sACCEPTED
000219207 980__ $$aCONF