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Project information
Learned Indexing for Similarity Searching

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.

Sustainable Development Goal No. 4 – Quality education Sustainable Development Goal No. 9 – Industry, innovation and infrastructure Sustainable Development Goal No. 16 – Peace, justice and strong institutions

Publications

Total number of publications: 17

2024

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