CS Events

Qualifying Exam

Multi-Modal Vector Query Processing

 

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Thursday, September 21, 2023, 01:30pm - 03:00pm
 

Speaker: Chaoji Zuo

Location : Core 301

Committee: 

Professor Dong Deng (Advisor)

Professor Yongfeng Zhang

Professor Amélie Marian

Professor Karl Stratos

Event Type: Qualifying Exam

Abstract: In recent years, various machine learning models, e.g., word2vec , doc2vec, and node2vec, have been developed to effectively represent real-world objects such as images, documents, and graphs as high-dimensional feature vectors. Simultaneously, these real-world objects frequently come with structured attributes or fields, such as timestamps, prices, and quantities. Many scenarios need to jointly query the vector representations of the objects together with their associated attributes.In this talk, I will outline our research efforts in the domains of range-filtering approximate nearest neighbor search (ANNS) and the construction of all-range approximate K-Nearest Neighbor Graphs (KNNG). In the context of range-filtering ANNS, queries are characterized by a query vector and a specified range within which the attribute values of data vectors must fall. We introduce an innovative indexing methodology addressing this challenge, encompassing ANNS indexes for all the potential query ranges. Our approach facilitates the retrieval of corresponding ANNS index within the query range, thereby improving query processing efficiency. Furthermore, we design an index to take a search key range as the query input and generate a KNNG graph composed of vectors falling within that specified query range. Looking ahead, our future work aims to develop a comprehensive database management system for vector data. This system will integrate all of our indexing techniques, providing durable storage and efficient querying capabilities.

Organization: 

Rutgers University

School of Arts and Sciences

Department of Computer Science

Contact  Professor Dong Deng