3 research outputs found
Benchmarking Learned Indexes
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 benchmark that compares well-tuned implementations of
three learned index structures against several state-of-the-art "traditional"
baselines. Using four real-world datasets, we demonstrate that learned index
structures can indeed outperform non-learned indexes in read-only in-memory
workloads over a dense array. We also investigate the impact of caching,
pipelining, dataset size, and key size. We study the performance profile of
learned index structures, and build an explanation for why learned models
achieve such good performance. Finally, we investigate other important
properties of learned index structures, such as their performance in
multi-threaded systems and their build times
Weiterentwicklung analytischer Datenbanksysteme
This thesis contributes to the state of the art in analytical database systems. First, we identify and explore extensions to better support analytics on event streams. Second, we propose a novel polygon index to enable efficient geospatial data processing in main memory. Third, we contribute a new deep learning approach to cardinality estimation, which is the core problem in cost-based query optimization.Diese Arbeit trägt zum aktuellen Forschungsstand von analytischen Datenbanksystemen bei. Wir identifizieren und explorieren Erweiterungen um Analysen auf Eventströmen besser zu unterstützen. Wir stellen eine neue Indexstruktur für Polygone vor, die eine effiziente Verarbeitung von Geodaten im Hauptspeicher ermöglicht. Zudem präsentieren wir einen neuen Ansatz für Kardinalitätsschätzungen mittels maschinellen Lernens