Papers in VLDB 2019 and TODS

Two new fantastic publications out of the ODIn Lab.

First, Niccolò Meneghetti's SIGMOD 2017 paper "Learning From Query-Answers: A Scalable Approach to Belief Updating and Parameter Learning," written in collaboration with Wolfgang Gatterbauer was invited as a "Best of SIGMOD" paper to ACM TODS. This paper has now been accepted (preprint here).

Next up, Ting Xie got a new VLDB 2019 paper: Query Log Compression for Workload Analytics. The paper explores techniques for compactly encoding lossy summaries of query logs for use in optimizers and workload analytics.