Proximity Rank Join

We introduce the proximity rank join problem, where we are given a set of relations whose tuples are equipped with a score and a real-valued feature vector. Given a target feature vector, the goal is to return the K combinations of tuples with high scores that are as close as possible to the target and to each other, according to some notion of distance. The setting closely resembles that of traditional rank join, but the geometry of the vector space plays a distinctive role in the computation of the overall score of a combination. Also, the input relations typically return their results either by distance from the target or by score. Because of these aspects, it turns out that traditional rank join algorithms, such as the well-known HRJN , have shortcomings in solving the proximity rank join problem, as they may read more input than needed. To overcome this weakness, we define a tight bound (used as a stopping criterion) that guarantees instance optimality, i.e., an I/O cost is achi...

• Type: Journal
• Year: 2010
• Where: PVLDB
• Authors: Davide Martinenghi, Marco Tagliasacchi
• Keywords: Feature Vector, PVLDB 2010, Rank Join, Tight Bound