LESIM (LEarned Segmentation Index with Multiple pointers)

Abstract

Indexes are essential for efficient information retrieval. This thesis presents LESIM (LEarned Segmentation Index with Multiple pointers), a learned structure for indexing records based on their timestamps. LESIM overcomes limitations of existing index structures, and supports efficient updates at the current time, as well as point queries over the past and the present. Extensive experiments were conducted based on two common real-world datasets. In comparison to the state-of-the-art learned Piecewise Geometric Model index (PGM), results demonstrate that LESIM provides a significant improvement in query and append performance. However, this comes at the cost of increased space consumption. The build time is competitive.

Date of Award	2023
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Dimitrios PAPADIAS (Supervisor)