Five papers and two demos by members of the Systems Group presented at VLDB 2019 in Los Angeles, CA, USA.

The following papers and two demos have been presented at VLDB 2019. In addition, a BIRTE  workshop (co-located with VLDB) paper, has been presented in Los Angeles, California.

VLDB 2019 Papers:

Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning Zeke Wang (ETH Zurich), Kaan Kara (ETH Zurich), Hantian Zhang (ETH Zurich), Gustavo Alonso (ETH Zurich), Onur Mutlu (ETH Zurich), and Ce Zhang (ETH Zurich)

ColumnML: Column-Store Machine Learning with On-The-Fly Data Transformation Kaan Kara (ETH Zurich), Ken Eguro (Microsoft), Ce Zhang (ETH Zurich), and Gustavo Alonso (ETH Zurich)

Megaphone: Latency-conscious state migration for distributed streaming dataflows Moritz Hoffmann (ETH Zurich), Andrea Lattuada (ETH Zurich), Frank McSherry (ETH Zurich), Vasiliki Kalavri (ETH Zurich), John Liagouris (ETH Zurich), and Timothy Roscoe (ETH Zurich)

Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms Ruoxi Jia (UC Berkeley), David Dao (ETH Zurich), Boxin Wang (Zhejiang University), Frances Ann Hubis (ETH Zurich), Nezihe Merve Gürel (ETH Zurich), Bo Li (University of Illinois at Urbana–Champaign), Ce Zhang (ETH Zurich), Costas J. Spanos (UC Berkeley), and Dawn Song (UC Berkeley)

VLDB 2019 DEMOS:

doppioDB 2.0: Hardware Techniques for Improved Integration of Machine Learning into Databases by Kaan Kara (ETH Zurich), Zeke Wang (ETH Zurich), Ce Zhang (ETH Zurich), and Gustavo Alonso (ETH Zurich)

Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization Cedric Renggli (ETH Zurich), Frances Ann Hubis (ETH Zurich), Bojan Karlaš (ETH Zurich), Kevin Schawinski (Modulos AG), Wentao Wu (Microsoft Research, Redmond), and Ce Zhang (ETH Zurich)

Workshop paper (BIRTE): 

FASTER State Management for Timely Dataflow Matthew J Brookes, Vasiliki Kalavri, John Liagouris (ETH Zurich)