This paper showed that the theory developed above, when combined with novel
engineering techniques, can be used to build an effective prototype inDB ML
system.

Authors: Maximilian Schleich, Dan Olteanu, Mahmoud Abo Khamis, Hung Q. Ngo,
XuanLong Nguyen. 2019.

In Proceedings of the 2019 International Conference on Management of Data
(SIGMOD ‘19).

This paper introduces LMFAO (Layered Multiple Functional Aggregate
Optimization), an in-memory optimization and execution engine for batches of
aggregates over the input database. The primary motivation for this work stems
from the observation that for a variety of analytics over databases, their
data-intensive tasks can be decomposed into group-by aggregates over the join of
the input database relations. We exemplify the versatility and competitiveness
of LMFAO for a handful of widely used analytics: learning ridge linear
regression, classi€cation trees, regression trees, and the structure of Bayesian
networks using Chow-Liu trees; and data cubes used for exploration in data
warehousing.

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A Layered Aggregate Engine for Analytics Workloads