Learned Indexing for Similarity Searching

• Applicant: Professor Dr. Peer Kröger
• Subject Area: Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 512436663

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.

• DFG Programme: Research Grants
• International Connection: Czech Republic
• Cooperation Partner: Professor Vlastislav Dohnal, Ph.D.