Processing Class-Constraint K-NN Queries with MISP

In this work, we consider processing k-nearest-neighbor (k-NN) queries, with the additional requirement that the result objects are of a speci fi c type. To solve this problem, we propose an approach based on a combination of an inverted index and state-of-the-art similarity search index structure for e ffi ciently pruning the search space early-on. Furthermore, we provide a cost model, and an extensive experimental study, that analyzes the performance of the proposed index structure under di ff erent con fi gurations, with the aim of fi nding the most e ffi cient one for the dataset being searched. ACM Reference Format: Evica Milchevski, Fabian Ne ff gen, and Sebastian Michel. 2018. Processing Class-Constraint K-NN Queries with MISP. InWebDB ' 18: 21st International Workshop on theWeb and Databases, June 10, 2018, Houston, TX, USA. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3201463.3201466