LESIM (LEarned Segmentation Index with Multiple pointers)

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


MPhil Thesis Defence


Title: "LESIM (LEarned Segmentation Index with Multiple pointers)"

By

Mr. Max PRIOR


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: 			Tuesday, 6 June 2023

Time: 			10:00am - 12:00noon

Venue: 			Room 3494
			lifts 25/26

Committee Members: 	Prof. Dimitris Papadias (Supervisor)
			Prof. Raymond Wong (Chairperson)
			Prof. Qiong Luo


**** ALL are Welcome ****