Events

PhD Defense Efficient and Scalable Management of Vector Data with Attribute Predicate Constraints
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Speaker: Chaoji Zuo

Bio

Location : CoRE 305

Committee: 

Assistant Professor Dong Deng

Associate Professor Desheng Zhang

Associate Professor Yongfeng Zhang

Erkang Zhu (external)

Event Type: PhD Defense

Abstract: The rise of high-dimensional vector representations, powered by advances in deep learning and representation learning, has reshaped how modern applications manage unstructured data such as text, images, and audio. Increasingly, these vector embeddings coexist with structured or semi-structured attributes—such as product metadata or image captions—creating a new class of hybrid data. While Approximate Nearest Neighbor Search (ANNS) and K-nearest neighbor graph (KNNG) construction remain foundational in vector data management, traditional indexing and retrieval techniques struggle with scalability and efficiency when handling complex queries involving diverse attribute predicates. This thesis presents a set of indexing techniques and frameworks designed to support efficient and flexible vector search under diverse predicate constraints. First, we introduce ARKGraph, a structure that compresses all possible range-aware KNN graphs, significantly reducing index size while maintaining low query latency. Second, we propose SeRF, a method that merges multiple ANNS indexes into a single compact structure, delivering efficient and consistent performance across a wide range of filtering selectivities on totally ordered attributes. Finally, we develop an attribute-agnostic search framework that integrates graph-based and inverted file indexing to support flexible queries over arbitrary predicates. Together, these contributions provide a scalable and adaptable foundation for managing hybrid datasets and enabling more expressive retrieval capabilities in real-world applications.

Organization: 

Contact  Assistant Professor Dong Deng

Zoom link:
https://rutgers.zoom.us/j/97509249123?pwd=OwFowtsleEKxycT6ml3S1q6nVsg9Pa.1