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 …

Learned indexes have attracted significant research interest due to their ability to offer better
space-time trade-offs compared to traditional B+-tree variants. Among various learned …

Machine-learning techniques, properly combined with data structures, have resulted in
Learned Static Indexes, innovative and powerful tools that speed up Binary Searches with …

The new area of Learned Data Structures consists of mixing Machine Learning techniques
with those specific to Data Structures, with the purpose to achieve time/space gains in the …

Learned Indexes use a model to restrict the search of a sorted table to a smaller interval.
Typically, a final binary search is done using the lower_bound routine of the Standard C++ …

Abstract Machine Learning Techniques, properly combined with Data Structures, have
resulted in Learned Static Indexes, innovative and powerful tools that speed-up Binary …

The rise of in-memory databases and systems with considerably large memories and cache
sizes requires the rethinking of the proper implementation of index structures like B+-trees in …

Today's processor landscape is increasingly heterogeneous with the availability of co-
processors. This landscape impacts query engines, as they need to be reworked to keep …

The amount of information which is gathered, pro cessed and sent by vehicles increases
permanently. Thereby, V2X communication is subject to various limitations such as limited …

Ever-since, database system engineers are striving for peak performance of their database
operators. However, this goal is a major endeavor since database operators are influenced …