Continuous Nearest Neighbor Queries over Sliding Windows

Abstract—This paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals (count-based) or 2) the arrivals within a fixed interval W covering the most recent time stamps (time-based). The task of the query processor is to constantly maintain the result of long-running NN queries among the valid data. We present two processing techniques that apply to both count-based and time-based windows. The first one adapts conceptual partitioning, the best existing method for continuous NN monitoring over update streams, to the sliding window model. The second technique reduces the problem to skyline maintenance in the distance-time space and precomputes the future changes in the NN set. We analyze the performance of both algorithms and extend them to variations of NN search. Finally, we compare their efficiency through a comprehensive experimental evaluation. The skyline-based algorithm achieves lower CPU cost, at the expense of slightly larger space overhead. Index Terms—Location-dependent and sensitive, spatial databases, query processing, nearest neighbors, data streams, sliding windows.

Spatial Network k-Nearest Neighbor: A Survey and Future Directives

Nearest neighbor algorithms play many roles in our daily lives. From facial recognition to networking applications, many of these are constantly improved for faster processing time and reliable memory management. There are many types of nearest neighbor algorithms. One of them is called k-nearest neighbor (k-NN), a technique that helps to find number of k closest objects from a user location within a specified range of area. k-NN road network algorithm studies have been through various query performance discussions. Each algorithm is usually judged based on query time over few selected parameters which are; number of k, network density and network size. Many studies have claimed different opinions over their techniques and with many results to prove better query performance than others. However, among these techniques, which k-NN road network algorithm has the highest rate of query performance based on the selected parameters? In this paper, reviews on several k nearest neighbor algorithms were made through series of journal extractions and experimentation in order to identify the algorithm that achieves highest query performance. It was found that with the experimentation method, we can identify not only the algorithm’s performance, but also its design flaws and possible future improvement. All methods were tested with some parameters such as varying number of k, road network density and network size. With the results collected, Incremental Expansion Restriction – Pruned Highway Labeling method (IER-PHL) proves to have the best query performance than other methods for most cases

VGQ-Vor: extending virtual grid quadtree with Voronoi diagram for mobile k nearest neighbor queries over mobile objects

Abstract Performing mobile k nearest neighbor (MkNN) queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried are mobile. The performance of this kind of query relies heavily on the maintenance of the current locations of the objects. The index used for mobile objects must support efficient update operations and efficient query handling. This study aims to improve the performance of the MkNN queries while reducing update costs. Our approach is based on an observation that the frequency of one region changing between being occupied or not by mobile objects is much lower than the frequency of the position changes reported by the mobile objects. We first propose an virtual grid quadtree with Voronoi diagram (VGQ-Vor), which is a two-layer index structure that indexes regions occupied by mobile objects in a quadtree and builds a Voronoi diagram of the regions. Then we propose a moving k nearest neighbor (kNN) query algorithm on the VGQ-Vor and prove the correctness of the algorithm. The experimental results show that the VGQ-Vor outperforms the existing techniques (Bx-tree, Bdual-tree) by one to three orders of magnitude in most cases

A Threshold-Based Algorithm for Continuous Monitoring of K Nearest Neighbors

Assume a set of moving objects and a central server that monitors their positions over time, while processing continuous nearest neighbor queries from geographically distributed clients. In order to always report up-to-date results, the server could constantly obtain the most recent position of all objects. However, this naive solution requires the transmission of a large number of rapid data streams corresponding to location updates. Intuitively, current information is necessary only for objects that may influence some query result (i.e., they may be included in the nearest neighbor set of some client). Motivated by this observation, we present a threshold-based algorithm for the continuous monitoring of nearest neighbors that minimizes the communication overhead between the server and the data objects. The proposed method can be used with multiple, static, or moving queries, for any distance definition, and does not require additional knowledge (e.g., velocity vectors) besides object locations