INSQ: An Influential Neighbor Set Based Moving kNN Query Processing System

TL;DR

The paper introduces INSQ, a system utilizing the Influential Neighbor Set algorithm for efficient moving kNN queries by reducing recomputation through safe guarding objects, applicable in Euclidean space and road networks.

Contribution

It proposes a novel Influential Neighbor Set algorithm that minimizes recomputation in moving kNN queries by using safe guarding objects instead of safe regions.

Findings

Reduces recomputation frequency in moving kNN queries.

Achieves high query processing efficiency in Euclidean and road network spaces.

Uses a small set of safe guarding objects to maintain kNN validity.

Abstract

We revisit the moving k nearest neighbor (MkNN) query, which computes one's k nearest neighbor set and maintains it while at move. Existing MkNN algorithms are mostly safe region based, which lack efficiency due to either computing small safe regions with a high recomputation frequency or computing larger safe regions but with a high cost for each computation. In this demonstration, we showcase a system named INSQ that adopts a novel algorithm called the Influential Neighbor Set (INS) algorithm to process the MkNN query in both two-dimensional Euclidean space and road networks. This algorithm uses a small set of safe guarding objects instead of safe regions. As long as the the current k nearest neighbors are closer to the query object than the safe guarding objects are, the current k nearest neighbors stay valid and no recomputation is required. Meanwhile, the region defined by the safe guarding objects is the largest possible safe region. This means that the recomputation frequency is also minimized and hence, the INS algorithm achieves high overall query processing efficiency.