Efficient k-nearest neighbor search on moving object trajectories

Abstract

With the growing number of mobile applications, data analysis on large sets of historical moving objects trajectories becomes increasingly important. Nearest neighbor search is a fundamental problem in spatial and spatio-temporal databases. In this paper we consider the following problem: Given a set of moving object trajectories D and a query trajectory mq, find the k nearest neighbors to mq within D for any instant of time within the life time of mq. We assume D is indexed in a 3D-R-tree and employ a filter-and-refine strategy. The filter step traverses the index and creates a stream of so-called units (linear pieces of a trajectory) as a superset of the units required to build the result of the query. The refinement step processes an ordered stream of units and determines the pieces of units forming the precise result.To support the filter step, for each node p of the index, in preprocessing a time dependent coverage function C p (t) is computed which is the number of trajectories represented in p present at time t. Within the filter step, sophisticated data structures are used to keep track of the aggregated coverages of the nodes seen so far in the index traversal to enable pruning. Moreover, the R-tree index is built in a special way to obtain coverage functions that are effective for pruning. As a result, one obtains a highly efficient k-NN algorithm for moving data and query points that outperforms the two competing algorithms by a wide margin.Implementations of the new algorithms and of the competing techniques are made available as well. Algorithms can be used in a system context including, for example, visualization and animation of results. Experiments of the paper can be easily checked or repeated, and new experiments be performed.

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

“…They consist of 276 and 145 trajectories, respectively. Each trajectory consist of location information for a truck/bus within a day, collected [12,14,19], we generate more objects moving on these trajectories as follows. Two groups of datasets are generated.…”

RFTraffic: a study of passive traffic awareness using emitted RF noise from the vehicles

“…To achieve this, we design two scenarios (static and dynamic) in this work to install a RF receiver either close to the road or inside the car to aggregate the emitted RF signals from the vehicles. In this work, we evaluate the recognition performance in both static and dynamic scenarios and discuss more about the three classification methods [5]: naive Bayes [6][7][8][9], decision tree [10][11][12] and k-nearest-neighbor [6,[13][14][15], to show the differences (advantage and disadvantage) of various classification algorithms in reality of traffic monitoring. As implemented, these classification methods are applied on the aggregated signal in the computer attached to the RF receiver to classify the traffic situation in both scenarios.…”

Key techniques for predicting the uncertain trajectories of moving objects with dynamic environment awareness

“…We have to improve these algorithms to adapt to unfixed networks or propose generic indexing structures. The newly proposed querying processing models for moving objects are detailedly discussed in [14][15][16].…”