SISAP 2023 Indexing Challenge – Learned Metric Index

• Authors: SLANINÁKOVÁ Terézia; PROCHÁZKA David; ANTOL Matej; OĽHA Jaroslav; DOHNAL Vlastislav
• Year of publication: 2023
• Type: Article in Proceedings
• Conference: Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289
• MU Faculty or unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1007/978-3-031-46994-7_24
• Keywords: sisap indexing challenge; learned metric index; similarity search; machine learning for indexing; performance benchmarking
• Description: This submission into the SISAP Indexing Challenge examines the experimental setup and performance of the Learned Metric Index, which uses an architecture of interconnected learned models to answer similarity queries. An inherent part of this design is a great deal of flexibility in the implementation, such as the choice of particular machine learning models, or their arrangement in the overall architecture of the index. Therefore, for the sake of transparency and reproducibility, this report thoroughly describes the details of the specific Learned Metric Index implementation used to tackle the challenge.

Related projects

• CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence
• e-Infrastruktura CZ
• Czech National Infrastructure for Biological Data
• Development of data processing techniques to support search, analysis and visualization of large datasets using artificial intelligence
• Learned Indexing for Similarity Searching
• Using artificial intelligence techniques for data processing, complex analysis and visualization of large-scale data