Project information
Learned Indexing for Similarity Searching

Information

This project doesn't include Faculty of Science. It includes Faculty of Informatics. Official project website can be found on muni.cz.

Project Identification

GF23-07040K

Project Period

7/2023 - 6/2026

Investor / Pogramme / Project type

Czech Science Foundation

LA Grants
Lead Agency

MU Faculty or unit

Faculty of Informatics

Cooperating Organization

University of Kiel

Responsible person prof. Dr. Peer Kröger

When faced with the task of storing and retrieving complex, unstructured or high-dimensional data (e.g., multimedia data), metric spaces are often employed as an underlying mathematical concept for their organization. Consequently, the only measure that can be used to arrange the data is a pairwise similarity between data objects. Similarity searching refers to a range of methods used to manage data enabling efficient search in such spaces. The main paradigm of similarity searching has remained mostly unchanged for decades -- data objects are organized into a hierarchical structure according to their mutual distances, using representative pivots to reduce the number of distance computations needed to efficiently search the data.

We plan to investigate an alternative to this paradigm, using machine learning models to replace pivots, thus, posing similarity search as a classification problem. We will use both supervised and unsupervised approaches to implement our solutions. We will also address the questions of scalability and dynamicity, and verify the applications for metric data.

Sustainable Development Goals

Masaryk University is committed to the UN Sustainable Development Goals, which aim to improve the conditions and quality of life on our planet by 2030.