Direct neighbor search

• We propose a novel query type called direct neighbor (DN) query.
• DN finds various applications, including competitor analysis and recommendation systems.
• We investigate DN and its variants (namely, K-DN and all-DN problems).
• We devise I/O optimal algorithms for DN and K-DN queries.
• We develop a novel, highly scalable algorithm for all-DN processing.

In this paper we study a novel query type, called direct neighbor query. Two objects in a dataset are direct neighbors (DNs) if a window selection may exclusively retrieve these two objects. Given a source object, a DN search computes all of its direct neighbors in the dataset. The DNs define a new type of affinity that differs from existing formulations (e.g., nearest neighbors, nearest surrounders, reverse nearest neighbors, etc.) and finds application in domains where user interests are expressed in the form of windows, i.e., multi-attribute range selections. Drawing on key properties of the DN relationship, we develop an I/O optimal processing algorithm for data indexed with a spatial access method. In addition to plain DN search, we also study its K-DN and all-DN variants. The former relaxes the DN condition – two objects are K  -DNs if a window query may retrieve them and only up to K−1K−1 other objects – whereas the all-DN variant computes the DNs of every object in the dataset. Using real, large-scale data, we demonstrate the efficiency and practicality of our approach, and show that it vastly outperforms a competitor constructed from previous work.