Five papers and a demo presented at VLDB 2017 in Munich

The following five papers and a demo have been presented at the 43rd International Conference on Very Large Data Bases VLDB 2017 in Munich, Germany.

"Distributed Join Algorithms on Thousands of Cores" by Claude Barthels (ETH), Ingo Müller (ETH), Timo Schneider (ETH), Gustavo Alonso (ETH), Torsten Hoefler (ETH)

“Caribou: Intelligent Distributed Storage” by Zsolt Istvan (ETH), David Sidler (ETH), Gustavo Alonso (ETH)

“Fast Scans on Key-Value Store”s by Markus Pilman (ETH), Kevin Bocksrocker (Microsoft), Lucas Braun (ETH), Renato Marroquın (ETH), Donald Kossmann (ETH)

“LDA*: A Robust and Large-scale Topic Modeling System” by Lele Yu (Peking University), Bin Cui (Peking University), Ce Zhang (ETH), Yingxia Shao (Peking University)

“An Experimental Evaluation of SimRank-based Similarity Search Algorithms” by Zhipeng Zhang (Peking University), Yingxia Shao (PKU), Bin Cui (Peking University), Ce Zhang (ETH)

DEMO: “MLog: Towards Declarative In-Database Machine Learning” by Xupeng Li (Peking University), Bin Cui (Peking University), Yiru Chen (Peking University), Wentao Wu (Microsoft Research), Ce Zhang (ETH)