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.

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GMOD (Generic Moving Objects Database) is a database system that manages moving objects traveling through different environments and with multiple transportation modes, like W alk → Car → Indoor, as humans' movement can cover several different environments (e.g., road network, indoor) instead of a single environment. To evaluate the performance of GMOD, a comprehensive and scalable dataset consisting of all available environments (e.g., roads, bus network, buildings) and moving objects with multiple modes is essentially needed, where the location of a moving object is represented by referencing to the underlying environment. Due to the difficulty of gaining real datasets, in this paper we present a tool that creates the overall space, which is composed of the following environments: road network, bus network, metro network, pavement areas and indoor. Each environment is also called an infrastructure. All outdoor infrastructures are produced from a real road dataset and the indoor environment consisting of a set of buildings is generated from public floor plans. Within each infrastructure, we design a graph as well as the algorithm for trip plannings, like indoor navigation, routing in bus network. The time complexity of the algorithm is also analyzed. A complete navigation system through all environments is developed, which is used to guide data generation for moving objects covering all available environments. The generated data, including all infrastructures and moving objects, is managed by GMOD. We report the experimental results of the data generator by conducting experiments on two real road datasets and a set of public floor plans.

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