LMFAO is an in-memory optimization and execution engine for large batches of group-by aggregates over joins. Such database workloads capture the data-intensive computation of a variety of data science applications. We demonstrate LMFAO for three popular models: ridge linear regression with batch gradient descent, decision trees with CART, and clustering with Rk-means.

翻译：LMFAO是大型集成集成合并后的一组集成的模拟优化和执行引擎,这种数据库工作量包含各种数据科学应用的数据密集计算。 我们为三种流行模型展示LMFAO:带分批梯度梯度的山脊线性回归、带CART的决定树和以Rk方式的集群。