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Found 8 papers, 4 papers with code
Detect, Distill and Update: Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
One open problem in this setting is how to update such ML models in the presence of data updates.
Towards Unbounded Machine Unlearning
This paper is the first, to our knowledge, to study unlearning for different applications (RB, RC, UP), with the view that each has its own desiderata, definitions for `forgetting' and associated metrics for forget quality.
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
One open problem in this setting is how to update such ML models in the presence of data updates.
Model Joins: Enabling Analytics Over Joins of Absent Big Tables
Q3: As the model join would be an approximation of the actual data join, how can one evaluate the quality of the model join result?
Graphical Join: A New Physical Join Algorithm for RDBMSs
The results for in-memory join computation show performance improvements up to 64X, 388X, and 6X faster than PostgreSQL, MonetDB and Umbra, respectively.
ML-AQP: Query-Driven Approximate Query Processing based on Machine Learning
As more and more organizations rely on data-driven decision making, large-scale analytics become increasingly important.
Databases
Adaptive Learning of Aggregate Analytics under Dynamic Workloads
Large organizations have seamlessly incorporated data-driven decision making in their operations.
Explaining Aggregates for Exploratory Analytics
Analysts wishing to explore multivariate data spaces, typically pose queries involving selection operators, i. e., range or radius queries, which define data subspaces of possible interest and then use aggregation functions, the results of which determine their exploratory analytics interests.
