We present the first learned index that supports predecessor, range queries and updates
within provably efficient time and space bounds in the worst case. In the (static) context of …

Recent advancements in learned index structures propose replacing existing index
structures, like B-Trees, with approximate learned models. In this work, we present a unified …

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 …

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 …

Non-Volatile Memory (NVM) has emerged as an alternative to next-generation main
memories. Although many tree indices have been proposed for NVM, they generally use B+ …

In modern solid-state drives (SSDs), the indexing of flash pages is a critical component in
their storage controllers. It not only affects the data access performance, but also determines …

We address the problem of designing, implementing, and experimenting with compressed
data structures that support rank and select queries over a dictionary of integers. We shine a …

A recent trend in algorithm design consists of augmenting classic data structures with
machine learning models, which are better suited to reveal and exploit patterns and trends …

Due to the high data volume and non-stationarity of time series data, it is very difficult to
directly use the original data for anomaly detection. In this study, a novel framework of …