Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the
precise execution of the corresponding downstream processes (inbound and outbound …

Data lakes are becoming increasingly prevalent for Big Data management and data
analytics. In contrast to traditional 'schema-on-write'approaches such as data warehouses …

Relational databases are the de facto standard for storing and querying structured data, and
extracting insights from structured data requires advanced analytics. Deep neural networks …

Differential privacy has garnered significant attention in recent years due to its potential in
offering robust privacy protection for individual data during analysis. With the increasing …

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 …

Motivated by fundamental applications in databases and relational machine learning, we
formulate and study the problem of answering functional aggregate queries (FAQ) in which …

Many IoT applications require the use of multiple deep neural networks (DNNs) to perform
various tasks on low-cost edge devices with limited computation resources. However …

Prediction over tabular data is often a crucial task in many real-life applications. Recent
advances in deep learning give rise to various deep models for tabular data prediction. A …

In line with the increasing machine learning model inference accuracy, deep learning (DL)
models have been increasingly applied to structured data for a wide spectrum of real-world …

User-defined functions (UDFs) have long been used as the de facto way to extend the
capabilities of data management systems. However, they are restricted to the specificities of …