Informace o projektu
Naučené indexy pro podobností hledání
- Kód projektu
- GF23-07040K
- Období řešení
- 7/2023 - 6/2026
- Investor / Programový rámec / typ projektu
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Grantová agentura ČR
- LA granty
- Lead agentura
- Fakulta / Pracoviště MU
- Fakulta informatiky
- Spolupracující organizace
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Christian-Albrechts-Universtät zu Kiel
- Odpovědná osoba 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.
Cíle udržitelného rozvoje
Masarykova univerzita se hlásí k cílům udržitelného rozvoje OSN, jejichž záměrem je do roku 2030 zlepšit podmínky a kvalitu života na naší planetě.
Publikace
Počet publikací: 17
2024
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Fast, structure-based searching in a large-scale protein data repository
Rok: 2024, druh: Konferenční abstrakty
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GraSR: Protein structural embeddings of AlphaFold DB
Rok: 2024, druh: Specializovaná databáze
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LMI-10: Protein structural embeddings of AlphaFold DB
Rok: 2024, druh: Specializovaná databáze
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LMI-30: Protein structural embeddings of AlphaFold DB
Rok: 2024, druh: Specializovaná databáze
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Scaling Learned Metric Index to 100M Datasets
17th International Conference on Similarity Search and Applications (SISAP 2024), rok: 2024
2023
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Reproducible experiments with Learned Metric Index Framework
Information systems, rok: 2023, ročník: 118, vydání: 1, DOI
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SISAP 2023 Indexing Challenge – Learned Metric Index
Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289, rok: 2023