{"type":"ScholarlyArticle","title":"App2Exa: Accelerating Exact kNN Search via Dynamic Cache-Guided Approximation","authors":[{"name":"Ke Li","slug":"ke-li","given":"Ke","family":"Li","affiliation":""},{"name":"Leong Hou U","slug":"leong-hou-u","given":"Leong Hou","family":"U","affiliation":""},{"name":"Shuo Shang","slug":"shuo-shang","given":"Shuo","family":"Shang","affiliation":""}],"abstract":"The k-nearest neighbor (kNN) query is a cornerstone of similarity-based applications across various domains. While prior work has enhanced kNN search efficiency, it typically focuses on approximate methods for high-dimensional data or exact methods for low-dimensional data, often assuming static query and data distributions. This creates a significant gap in accelerating exact kNN search for low-to-medium dimensional data with dynamic query distributions. To fill this gap, we propose App2Exa, a cache-guided framework that integrates approximate and exact kNN search. App2Exa utilizes a dynamically maintained cache graph index to retrieve approximate results, which subsequently guide exact search using a VP-Tree with a best-first strategy. A benefit-driven caching mechanism further optimizes performance by prioritizing vectors based on frequency, recency, and computational cost. Experimental results demonstrate that App2Exa significantly boosts efficiency, providing a robust and scalable solution for evolving query patterns and enabling exact kNN search to support higher dimensionality more effectively.","doi":"10.24963/ijcai.2025/339","url":"https://ijcai.org/proceedings/2025/339","pdf":"https://ijcai.org/proceedings/2025/0339.pdf","markdown":"https://ijcai.org/proceedings/2025/339.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/339","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":339,"pages":{"first":3045,"last":3053},"keywords":["Multidisciplinary Topics and Applications: MTA: Databases","Multidisciplinary Topics and Applications: MTA: Transportation","Data Mining: DM: Information retrieval","Data Mining: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}