Filtering data based on predicates is one of the most fundamental operations for any modern
data warehouse. Techniques to accelerate the execution of filter expressions include …

Index plays an essential role in modern database engines to accelerate the query
processing. The new paradigm of" learned index" has significantly changed the way of …

We introduce BOURBON, a log-structured merge (LSM) tree that utilizes machine learning to
provide fast lookups. We base the design and implementation of BOURBON on empirically …

RDMA (Remote Direct Memory Access) has gained considerable interests in network-
attached in-memory key-value stores. However, traversing the remote tree-based index in …

Indexes can improve query-processing performance by avoiding full table scans. Although
traditional indexes (eg, B+-tree) have been widely used, learned indexes are proposed to …

Index structures in memory systems become important to improve the entire system
performance. The promising learned indexes leverage deep-learning models to …

Disaggregated memory systems separate monolithic servers into different components,
including compute and memory nodes, to enjoy the benefits of high resource utilization …

The recently released persistent memory (PM) offers high performance, persistence, and is
cheaper than DRAM. This opens up new possibilities for indexes that operate and persist …

Cardinality estimation is a fundamental and critical problem in databases. Recently, many
estimators based on deep learning have been proposed to solve this problem and they have …

Recently, Machine Learning (ML, for short) has been successfully applied to database
indexing. Initial experimentation on Learned Indexes has demonstrated better search …