Predicting personalized sequential behavior is a key task for recommender systems. In order
to predict user actions such as the next product to purchase, movie to watch, or place to visit …

Many organizations are shifting to a data management paradigm called the" Lakehouse,"
which implements the functionality of structured data warehouses on top of unstructured …

This work aims at reducing the main-memory footprint in high performance hybrid OLTP &
OLAP databases, while retaining high query performance and transactional throughput. For …

In-memory database management systems (DBMSs) are a key component of modern on-
line analytic processing (OLAP) applications, since they provide low-latency access to large …

This paper presents iDO, a compiler-directed approach to failure atomicity with nonvolatile
memory. Unlike most prior work, which instruments each store of persistent data for redo or …

There has been significant amount of excitement and recent work on GPU-based database
systems. Previous work has claimed that these systems can perform orders of magnitude …

The query engines of most modern database systems are either based on vectorization or
data-centric code generation. These two state-of-the-art query processing paradigms are …

In-memory databases require careful tuning and many engineering tricks to achieve good
performance. Such database performance engineering is hard: a plethora of data and …

In the presence of exponential growth of the data produced every day in volume, velocity,
and variety, online analytical processing (OLAP) is becoming increasingly challenging …

The huge demand for computation in artificial intelligence (AI) is driving unparalleled
investments in hardware and software systems for AI. This leads to an explosion in the …