A Distributed Approach to Continuous Monitoring of Constrained k-Nearest Neighbor Queries in Road Networks

Abstract

Given two positive parameters k and r, a constrained k-nearest neighbor (CkNN) query returns the k closest objects within a network distance r of the query location in road networks. In terms of the scalability of monitoring these CkNN queries, existing solutions based on central processing at a server suffer from a sudden and sharp rise in server load as well as messaging cost as the number of queries increases. In this paper, we propose a distributed and scalable scheme called DAEMON for the continuous monitoring of CkNN queries in road networks. Our query processing is distributed among clients (query objects) and server. Specifically, the server evaluates CkNN queries issued at intersections of road segments, retrieves the objects on the road segments between neighboring intersections, and sends responses to the query objects. Finally, each client makes its own query result using this server response. As a result, our distributed scheme achieves close-to-optimal communication costs and scales well to large numbers of monitoring queries. Exhaustive experimental results demonstrate that our scheme substantially outperforms its competitor in terms of query processing time and messaging cost.

Visibility Detection of 3D Objects and Visual K-Nearest Neighbor Query Based on Convex Hull Model

“…e studies above were more suitable for centralized data processing. Cho et al [21] proposed a distributed expansion scheme of DAEMON, which could be used for CkNN query in the road network. Vorona et al [22] proposed an approximate geospatial query processing engine based on deep learning, which could respond to flexible aggregation queries, but the study did not consider the issue of visibility.…”

Moving range k nearest neighbor queries with quality guarantee over uncertain moving objects

“…Paralleling the advances in positioning and wireless network technologies, peer-to-peer (P2P) query processing has attracted many techniques for location-based queries (e.g., range queries [26], k-NN queries [7,8,9,11,23,27,29], and top-k queries [22]) in a mobile P2P environment, where neither fixed communication infrastructures nor centralized/distributed servers are available. In this paper, we study Moving Range k-Nearest Neighbor (MRkNN) queries in a mobile P2P environment.…”

A privacy-aware monitoring algorithm for moving $$k$$ k -nearest neighbor queries in road networks

“…Therefore, several methods have been proposed for protecting the privacy of mobile users who issue location-based queries in a road network [13,[28][29][30][31]33,37,40]. According to the query lifetime, a locationbased query can be classified further as either a snapshot query (e.g., "tell me the locations of the three nearest gasoline stations") or a continuous query (e.g., "monitor the three closest restaurants for the next 10 min") [7,34]. A snapshot query returns a result only once, whereas a continuous query returns the results continuously for a designated period of time.…”