A highly optimized algorithm for continuous intersection join queries over moving objects

Abstract

Given two sets of moving objects with nonzero extents, the continuous intersection join query reports every pair of intersecting objects, one from each of the two moving object sets, for every timestamp. This type of queries is important for a number of applications, e.g., in the multi-billion dollar computer game industry, massively multiplayer online games like World of Warcraft need to monitor the intersection among players' attack ranges and render players' interaction in real time. The computational cost of a straightforward algorithm or an algorithm adapted from another query type is prohibitive, and answering the query in real time poses a great challenge. Those algorithms compute the query answer for either too long or too short a time interval, which results in either a very large computation cost per answer update or too frequent answer updates, respectively. This observation motivates us to optimize the query processing in the time dimension. In this study, we achieve this optimization by introducing the new concept of time-constrained (TC) processing. Further, TC processing enables a set of effective improvement techniques on traditional intersection join algorithms. Finally, we provide a method to find the optimal value for an important parameter required in our technique, the maximum update interval. As a result, we achieve a highly optimized algorithm for processing continuous intersection join queries on moving objects. With a thorough experimental study, we show that our algorithm outperforms the best adapted existing solution by several orders of magnitude. We also validate the accuracy of our cost model and its effectiveness in optimizing the performance.

Real-Time Spatial Queries for Moving Objects Using Storm Topology

“…Unlike static spatial queries, continuous queries [42,43] can be classified into three types-moving queries on stationary objects, stationary queries on moving objects and moving queries on moving objects [44]. In our research, we regard the result of a continuous query as a continuous series of snapshots of a set of results, which is calculated based on the constantly changing spatial positions of the moving objects of interest.…”

“…A distributed index data update actually consists of three processes [44]: 1. Perform add, update or delete operations on the secondary indexes where the point to be updated is stored; 2.…”

Processing moving k NN queries using influential neighbor sets

“…In recent years, other types of queries on moving objects have also been studied extensively. These include the range queries [1,26], the kNN queries with two predicates [2], the density queries [10], the intersection join queries [27,28], the obstructed NN queries [5,13], the visible NN queries [6], the weighted NN queries [14] and the destination prediction queries [24], etc. These studies have different problem settings from ours and their solutions are inapplicable.…”

Modeling and Querying Moving Objects with Social Relationships

“…Moving object databases (MOD) allow modelling, manage moving entities such as people, vehicles and vessels, and have been extensively studied over the past few years, such as data modelling [1][2][3][4][5][6], indexing [7][8][9][10] , and querying [11][12][13][14][15]. This research focuses on the management of movements and involved geographic environments, but ignores the social relationships between moving objects.…”