13 citations · 17 across the 5 of their papers we have counts for
5 papers
AnyDB: An Architecture-less DBMS for Any Workload
Tiemo Bang, Norman May, Ilia Petrov +1
In this paper, we propose a radical new approach for scale-out distributed DBMSs. Instead of hard-baking an architectural model, such as a shared-nothing architecture, into the dis…
Learning a Partitioning Advisor with Deep Reinforcement Learning
Benjamin Hilprecht, Carsten Binnig, Uwe Roehm
Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the c…
Controlling False Discoveries During Interactive Data Exploration
Zheguang Zhao, Lorenzo De Stefani, Emanuel Zgraggen +3
Recent tools for interactive data exploration significantly increase the chance that users make false discoveries. The crux is that these tools implicitly allow the user to test a…
Revisiting Reuse in Main Memory Database Systems
Kayhan Dursun, Carsten Binnig, Ugur Cetintemel +1
Reusing intermediates in databases to speed-up analytical query processing has been studied in the past. Existing solutions typically require intermediate results of individual ope…
The End of a Myth: Distributed Transactions Can Scale
Erfan Zamanian, Carsten Binnig, Tim Kraska +1
The common wisdom is that distributed transactions do not scale. But what if distributed transactions could be made scalable using the next generation of networks and a redesign of…