13 citations · 31 across the 8 of their papers we have counts for
7 papers · 1 filter
Optimizing Tensor Programs on Flexible Storage
Maximilian Schleich, Amir Shaikhha, Dan Suciu
Tensor programs often need to process large tensors (vectors, matrices, or higher order tensors) that require a specialized storage format for their memory layout. Several such lay…
LMFAO: An Engine for Batches of Group-By Aggregates
Maximilian Schleich, Dan Olteanu
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 var…
Learning Models over Relational Data: A Brief Tutorial
Maximilian Schleich, Dan Olteanu, Mahmoud Abo-Khamis +2
This tutorial overviews the state of the art in learning models over relational databases and makes the case for a first-principles approach that exploits recent developments in da…
A Layered Aggregate Engine for Analytics Workloads
Maximilian Schleich, Dan Olteanu, Mahmoud Abo Khamis +2
This paper introduces LMFAO (Layered Multiple Functional Aggregate Optimization), an in-memory optimization and execution engine for batches of aggregates over the input database.…
Functional Aggregate Queries with Additive Inequalities
Mahmoud Abo Khamis, Ryan R. Curtin, Benjamin Moseley +4
Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some…
AC/DC: In-Database Learning Thunderstruck
Mahmoud Abo Khamis, Hung Q. Ngo, XuanLong Nguyen +2
We report on the design and implementation of the AC/DC gradient descent solver for a class of optimization problems over normalized databases. AC/DC decomposes an optimization pro…
Learning Models over Relational Data using Sparse Tensors and Functional Dependencies
Mahmoud Abo Khamis, Hung Q. Ngo, XuanLong Nguyen +2
Integrated solutions for analytics over relational databases are of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily b…