With the advances in wireless Internet and mobile positioning technology, location-based services (LBSs) have become popular. In LBSs, users must send their exact locations in order to use the services, but they may be subject to several privacy threats. To solve this problem, query processing algorithms based on a cloaking method have been proposed. The algorithms use spatial cloaking methods to blur the user's exact location in a region satisfying the required privacy threshold (k). With the cloaked region, an LBS server can execute a spatial query processing algorithm preserving their privacy. However, the existing algorithms cannot provide good query processing performance. To resolve this problem, we, in this paper, propose a k-NN query processing algorithm based on network Voronoi diagram for spatial networks. Therefore, our algorithm can reduce network expansion overhead and share the information of the expanded road network. In order to demonstrate the efficiency of our algorithms, we have conducted extensive performance evaluations. The results show that our algorithm achieves better performance on retrieval time than the existing algorithms, such as PSNN and kRNN. This is because our k-NN query processing algorithm can greatly reduce a network expansion cost for retrieving k POIs.

Even though it is very important to retrieve similar trajectories with a given query trajectory, there has been a little research on trajectory retrieval in spatial networks, like road networks. In this paper, we propose an efficient indexing scheme for retrieving moving object trajectories in spatial networks. For this, we design a signature-based indexing scheme for efficiently dealing with the trajectories of current moving objects as well as for maintaining those of past moving objects. In addition, we provide an insertion algorithm for storing the segment information of a moving object trajectory as well as a retrieval algorithm to find a set of moving objects whose trajectories match the segments of a query trajectory. Finally, we show that our signature-based indexing scheme achieves at least twice better performance on trajectory retrieval than the leading trajectory indexing schemes, such as TB-tree, FNR-tree, and MON-tree.

The deployment of historical trajectories of moving objects has greatly increased for various applications in road networks. For instance, similar patterns of moving-object trajectories are very useful for designing the transportation network of a new city. In this paper, we define a spatio-temporal similarity measure based on a road network distance, rather than a Euclidean distance. We also propose a new similar trajectory search algorithm based on the spatio-temporal measure by using an efficient pruning mechanism. Finally, we show the efficiency of our algorithm, both in terms of retrieval accuracy and retrieval efficiency.

Recently, the join processing of large-scale datasets in MapReduce environments has become an important issue. However, the existing MapReduce-based join algorithms suffer from too much overhead for constructing and updating the data index. Moreover, the similarity computation cost is high because the existing algorithms partition data without considering the data distribution. In this paper, we propose two grid-based join algorithms for MapReduce. First, we propose a similarity join algorithm that evenly distributes join candidates using a dynamic grid index, which partitions data considering data density and similarity threshold. We use a bottom-up approach by merging initial grid cells into partitions and assigning them to MapReduce jobs. Second, we propose a k-NN join query processing algorithm for MapReduce. To reduce the data transmission cost, we determine an optimal grid cell size by considering the data distribution of randomly selected samples. Then, we perform kNN join by assigning the only related join data to a reducer. From performance analysis, we show that our similarity join query processing algorithm and our k-NN join algorithm outperform existing algorithms by up to 10 times, in terms of query processing time.

To analyze large-scale data efficiently, studies on Hadoop, one of the most popular MapReduce frameworks, have been actively done. Meanwhile, most of the large-scale data analysis applications, e.g., data clustering, are required to do the same map and reduce functions repeatedly. However, Hadoop cannot provide an optimal performance for iterative MapReduce jobs because it derives a result by doing one phase of map and reduce functions. To solve the problems, in this paper, we propose a new efficient resource management framework for iterative MapReduce processing in large-scale data analysis. For this, we first design an iterative job state-machine for managing the iterative MapReduce jobs. Secondly, we propose an invariant data caching mechanism for reducing the I/O costs of data accesses. Thirdly, we propose an iterative resource management technique for efficiently managing the resources of a Hadoop cluster. Fourthly, we devise a stop condition check mechanism for preventing unnecessary computation. Finally, we show the performance superiority of the proposed framework by comparing it with the existing frameworks.

Although a large number of query processing algorithms in spatial network database (SNDB) have been studied, there exists little research on route-based queries. Since moving objects move only in spatial networks, route-based queries, like in-route nearest neighbor (IRNN), are essential for Location-based Service (LBS) and Telematics applications. However, the existing IRNN query processing algorithm has a problem in that it does not consider time and space constraints. Therefore, we, in this paper, propose IRNN query processing algorithms which take both time and space constraints into consideration. Finally, we show the effectiveness of our IRNN query processing algorithms considering time and space constraints by comparing them with the existing IRNN algorithm.

Since wireless sensor networks (WSNs) are resources-constrained, it is very essential to gather data efficiently from the WSNs so that their life can be prolonged. Data aggregation can conserve a significant amount of energy by minimizing transmission cost in terms of the number of data packets. Many applications require privacy and integrity protection of the sampled data while they travel from the source sensor nodes to a data collecting device, say a query server. However, the existing schemes suffer from high communication cost, high computation cost and data propagation delay. To resolve the problems, in this paper, we propose a new and efficient integrity protecting sensitive data aggregation scheme for WSNs. Our scheme makes use of the additive property of complex numbers to achieve sensitive data aggregation with protecting data integrity. With simulation results, we show that our scheme is much more efficient in terms of both communication and computation overheads, integrity checking and data propagation delay than the existing schemes for protecting integrity and privacy preserving data aggregation in WSNs.