TY - GEN
T1 - Domain query optimization
T2 - Datenbanksysteme fur Business, Technologie und Web, BTW 2019 and 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme", DBIS 2019 - Database Systems for Business, Technology and Web, BTW 2019 and 18th Symposium of the GI Department "Databases and Information Systems", DBIS 2019
AU - Vogelsgesang, Adrian
AU - Muehlbauer, Tobias
AU - Leis, Viktor
AU - Neumann, Thomas
AU - Kemper, Alfons
N1 - Publisher Copyright:
© 2019 Gesellschaft fur Informatik (GI). All rights reserved.
PY - 2019
Y1 - 2019
N2 - The Hyper database system was started as an academic project at Technical University Munich. In 2016, the commercial spin-off of the academic Hyper database system was acquired by Tableau, a leader in the analytics and business intelligence (BI) platforms market. As a human-in-the-loop BI platform, Tableau products machine-generate query workloads with characteristics that differ from human-written queries and queries represented in industry-standard database system benchmarks. In this work, we contribute optimizations we developed for one important class of queries typically generated by Tableau products: retrieving (aggregates of) the domain of a column. We devise methods for leveraging the compression of the database column in order to efficiently retrieve the duplicate-free value set, i.e., the domain. Our extensive performance evaluation of a synthetic benchmark and over 60 thousand real-world workbooks from Tableau Public shows that our optimization enables query latencies for domain queries that allow self-service ad-hoc data exploration.
AB - The Hyper database system was started as an academic project at Technical University Munich. In 2016, the commercial spin-off of the academic Hyper database system was acquired by Tableau, a leader in the analytics and business intelligence (BI) platforms market. As a human-in-the-loop BI platform, Tableau products machine-generate query workloads with characteristics that differ from human-written queries and queries represented in industry-standard database system benchmarks. In this work, we contribute optimizations we developed for one important class of queries typically generated by Tableau products: retrieving (aggregates of) the domain of a column. We devise methods for leveraging the compression of the database column in order to efficiently retrieve the duplicate-free value set, i.e., the domain. Our extensive performance evaluation of a synthetic benchmark and over 60 thousand real-world workbooks from Tableau Public shows that our optimization enables query latencies for domain queries that allow self-service ad-hoc data exploration.
UR - http://www.scopus.com/inward/record.url?scp=85072101871&partnerID=8YFLogxK
U2 - 10.18420/btw2019-19
DO - 10.18420/btw2019-19
M3 - Conference contribution
AN - SCOPUS:85072101871
T3 - Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
SP - 313
EP - 333
BT - Datenbanksysteme fur Business, Technologie und Web, BTW 2019 and 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme", DBIS 2019
A2 - Grust, Torsten
A2 - Naumann, Felix
A2 - Bohm, Alexander
A2 - Lehner, Wolfgang
A2 - Harder, Theo
A2 - Rahm, Erhard
A2 - Heuer, Andreas
A2 - Klettke, Meike
A2 - Meyer, Holger
PB - Gesellschaft fur Informatik (GI)
Y2 - 4 March 2019 through 8 March 2019
ER -