@inproceedings{3104953eed404bd5b174d987b0ce6295,
title = "Aggregate k Nearest Neighbor Queries in Metric Spaces",
abstract = "Aggregate k nearest neighbor (AkNN) queries are useful in many areas, such as multimedia retrieval and resource allocation, to name but a few. Most of existing works on AkNN query only focus on Euclidean space or specific metric space, which employ properties of particular data to accelerate the query. However, due to the complex data types involved and the needs for flexible similarity criteria seen in real applications, properties of particular data cannot be used for general case. Hence, in this paper, we investigate AkNN search in metric spaces, termed as metric AkNN (MAkNN) search, as metric spaces can support any type of data and flexible similarity criteria as long as satisfying triangle inequality. To efficiently answer MAkNN queries, we develop several pruning techniques and corresponding algorithms based on SPB-tree. Extensive experiments using three real data sets verify the efficiency of our MAkNN algorithms.",
keywords = "Aggregate k nearest neighbor query, Algorithm, Metric space",
author = "Xin Ding and Yuanliang Zhang and Lu Chen and Keyu Yang and Yunjun Gao",
year = "2018",
doi = "10.1007/978-3-319-96893-3_24",
language = "English",
isbn = "978-3-319-96892-6",
volume = "2",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "317--333",
editor = "Yi Cai and Yoshiharu Ishikawa and Jianliang Xu",
booktitle = "Web and Big Data",
address = "Germany",
note = "Second International Joint Conference, APWeb-WAIM 2018 ; Conference date: 23-07-2018 Through 25-07-2018",
}