Distributed in-memory processing of All K Nearest Neighbor queries

A wide spectrum of Internet-scale mobile applications, ranging from social networking, gaming and entertainment to emergency response and crisis management, all require efficient and scalable All k Nearest Neighbor (AkNN) computations over millions of moving objects every few seconds to be operational. In this paper we present Spitfire, a distributed algorithm that provides a scalable and high-performance AkNN processing framework to our award-winning geo-social network named Rayzit. The proposed algorithm deploys a fast load-balanced partitioning along with an efficient replication-set selection, to provide fast main-memory computations of the exact AkNN results in a batch-oriented manner. We evaluate, both analytically and experimentally, how the pruning efficiency of the Spitfire algorithm plays a pivotal role in reducing communication and response time up to an order of magnitude, compared to three state-of-the-art distributed AkNN algorithms executed in distributed main-memory.