Query Radius Research Articles

Dwell Regions: Generalized Stay Regions for Streaming and Archival Trajectory Data

A region ℛ is a dwell region for a moving objectOif, given a threshold distancerqand duration τq, every point of ℛ remains within distancerqfromOfor at least time τq. Points within ℛ are likely to be of interest toO, so identification of dwell regions has applications such as monitoring and surveillance. We first present a logarithmic-time online algorithm to find dwell regions in an incoming stream of object positions. Our method maintains the upper and lower bounds for the radius of the smallest circle enclosing the object positions, thereby greatly reducing the number of trajectory points needed to evaluate the query. It approximates the radius of the smallest circle enclosing a given subtrajectory within an arbitrarily small user-defined factor and is also able to efficiently answer decision queries asking whether or not a dwell region exists. For the offline version of the dwell region problem, we first extend our online approach to develop the ρ-Index, which indexes subtrajectories using query radius ranges. We then refine this approach to obtain the τ-Index, which indexes subtrajectories using both query radius ranges and dwell durations. Our experiments using both real-world and synthetic datasets show that the online approach can scale up to hundreds of thousands of moving objects. For archived trajectories, our indexing approaches speed up queries by many orders of magnitude.

A PID-Based kNN Query Processing Algorithm for Spatial Data.

As a popular spatial operation, the k-Nearest Neighbors (kNN) query is widely used in various spatial application systems. How to efficiently process a kNN query on spatial big data has always been an important research topic in the field of spatial data management. The centralized solutions are not suitable for spatial big data due to their poor scalability, while the existing distributed solutions are not efficient enough to meet the high real-time requirements of some spatial applications. Therefore, we introduce the Proportional Integral Derivative (PID) control technology into kNN query processing and propose a PID-based kNN query processing algorithm (PIDKNN) for spatial big data based on Spark. In this algorithm, the whole data space is divided into grid cells of the same size using the grid partition method, and the grid-based index is constructed. On this basis, the grid-based density peak clustering algorithm is used to cluster spatial data, and the corresponding PID parameters are set for each cluster. When performing kNN queries, the PID algorithm is used to estimate the radius growth step size of kNN queries, thereby realizing kNN query processing with a variable query radius growth step based on a feedback mechanism, which greatly improves the efficiency of kNN query processing. A series of experimental results show that the PIDKNN algorithm has good performance and scalability and is superior to the existing parallel kNN query processing methods.

Deep learning network for point cloud classification based on k-dimensional tree neighbor query

PointNet++ is one of the effective deep learning methods to deal with the point cloud classification. However, there are some problems in this method including low precision, being time-consuming and sensitive to the noise of input points. In order to deal with these problems, we propose an efficient neighbor query method in which a k-dimensional tree (kd tree) structure is constructed to find the neighbor points within a specified radius around the query points, and the local features are extracted at the grouping layer of PointNet++. Aiming at the overfitting problem existing in the original network training process, we introduce the dropout regularization and thus reduce the training time of network convergence. The experiment environment is TensorFlow framework under Ubuntu14.04 system. The training and testing experiments are conducted based on ModelNet40 dataset. The classification accuracy of this method reaches 91.1%, 92.1% and 94.3% when the query radius is 0.1, 0.2 and 0.3, which is higher than PointNet++, and the kd tree method takes less time than the original method. At the same time, the improved structure has better semantic segmentation performance on the Stanford 3D semantic analysis dataset (mean intersection over union reaches 57.2%), which demonstrates the proposed method being more robust to occlusion objects.

A personalized range‐sensitive privacy‐preserving scheme in LBSs

SummaryMobile social network has become a necessary part in our daily life, and location‐based services (LBSs) provide unprecedented convenience to mobile users. However, these attracting services are accompanied with privacy disclosures, including location privacy and query privacy. Mobile users have to expose their personal information to untrusted location‐based service provider (LSP) in order to obtain relevant service data. To address these privacy issues, we proposed a personalized range‐sensitive privacy‐preserving scheme, called PRPS, which considers the relationship between locations, query ranges, and query contents. Moreover, PRPS employs map storing algorithm (MSA) to facilitate the storage of two‐dimensional local map, reducing the cost of storage. Furthermore, range estimating algorithm (REA) adopts binary quad‐tree to decide the query radius of each submitted location, avoiding inference attacks by adversary. The requirements generating algorithm (RGA) selects relevant query content for each dummy location, guaranteeing mobile user's location privacy and query privacy. Finally, we illustrate the privacy analysis to proof PRPS's privacy degree; then, the performance and privacy evaluation results indicate that the proposed PRPS is effective and efficient.

Collaborative location recommendations with dynamic time periods

This study proposes a location-based collaborative filtering recommendation system with dynamic time periods (LCFDTPs) for recommending timely and suitable points of interest (POIs) to mobile users. The system expedites calculating similarity based on POI recency and enables mobile users to promptly obtain recommended items that closely match their current space–time conditions by selecting different strategies for dissimilar situations. The performance of the proposed system was evaluated through simulations. The simulation results revealed that compared with three existing strategies, the proposed LCFDTP system demonstrated higher recommendation accuracy and coverage and a shorter average recommendation time. When the users’ moving velocity was set to 50 km/h and the query radius was set to 2 km, the recommendation precision of the proposed system was 61% higher than those of the other strategies. Moreover, the recommendation coverage and average response time of the LCFDTP system were 9% higher and 62% shorter than those of the other compared strategies, respectively. The LCFDTP system can also improve the POI recommendation quality by applying location-based services, thus enhancing user satisfaction.

A new set of pivot elimination schemes for increasing the query performance

M-Tree, Slim-Tree, DF-Tree, and Omni-Tree are some of the popular dynamic structures which can grow incrementally by splitting overflowed nodes, and adding new levels to the tree very much like the B-tree variants. Unfortunately, they have been shown to perform very poorly compared to flat structur es such as AESA, LAESA, Spaghettis, and Kvp that use a fixed set of global pivots. HKvp index structure is an extension of Kvp allowing the elimination of pivots as well as the database objects. The number of pivots can be easily increased to provide more selectivity and query performance. However, there is an optimum number of pivots for a given query radius, and using too many pivots increases the costs of queries and index initialization. In this paper, a new set of pivot elimination mechanisms is proposed to determine the right number of pivots for different query radii. The suggested pivot elimination schemes perform significant cost reduction in terms of number of distance computations, and they estimate the drop rate value for HKvp on query time.

Quicker range- and k-NN joins in metric spaces

We consider the join operation in metric spaces. Given two sets A and B of objects drawn from some universe U, we want to compute the set A⋈B={(a,b)∈A×B|d(a,b)≤r} efficiently, where d:U×U→R+ is a metric distance function and r∈R+ is user supplied query radius. In particular we are interested in the case where we have no index available (nor we can afford to build it) for either A or B. In this paper we improve the Quickjoin algorithm [6], inspired by the well-known Quicksort algorithm, by (i) replacing the low level component that handles small subsets with essentially brute-force nested loop with a more efficient method; (ii) showing that, contrary to Quicksort, in Quickjoin unbalanced partitioning can improve the algorithm; (iii) making the algorithm probabilistic while still obtaining most of the relevant results; and (iv) improving the (worst case) work space needed, from O(n2) to O(logn), where n is the size of the dataset(s). We also discuss pivot selection, show how to adapt Quickjoin to compute k-nearest neighbor joins and sketch a parallel version of the algorithm. The experimental results show that the method works well in practice.

An encoding-based dual distance tree high-dimensional index

The paper proposes a novel symmetrical encoding-based index structure, which is called EDD-tree (for encoding-based dual distance tree), to support fast k-nearest neighbor (k-NN) search in high-dimensional spaces. In the EDD-tree, all data points are first grouped into clusters by a k-means clustering algorithm. Then the uniform ID number of each data point is obtained by a dual-distance-driven encoding scheme, in which each cluster sphere is partitioned twice according to the dual distances of start-and centroid-distance. Finally, the uniform ID number and the centroid-distance of each data point are combined to get a uniform index key, the latter is then indexed through a partition-based B+-tree. Thus, given a query point, its k-NN search in high-dimensional spaces can be transformed into search in a single dimensional space with the aid of the EDD-tree index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of our proposed scheme, and the results demonstrate that this method outperforms the state-of-the-art high-dimensional search techniques such as the X-tree, VA-file, iDistance and NB-tree, especially when the query radius is not very large.

Answering k-NN Query of Chinese Calligraphic Character Based on Data Grid

In this paper, a novel k-Nearest Neighbor (k-NN) query over the Chinese calligraphic character databases based on Data Grid is proposed. First when user in the query node submits a query character and k, the character filtering algorithm is performed using the hybrid distance metric (HDM) index. Then the candidate characters are transferred to the executing nodes in a package mode. Furthermore, the refinement process of the candidate characters is conducted in parallelism to get the answer set. Finally, the answer set is transferred to the query node. If the number of answer set is less than k, then the query procedure is re-performed by increasing the query radius until the k nearest neighbor characters are obtained. The analysis and experimental results show that the performance of the algorithm is good in minimizing the response time by decreasing network transfer cost and increasing parallelism of I/O and CPU.

Efficient Near Neighbor Searching Using Multi-Indexes for Content-Based Multimedia Data Retrieval

Many content-based multimedia data retrieval problems can be transformed into the near neighbor searching problem in multidimensional feature space. An efficient near neighbor searching algorithm is needed when developing a multimedia database system. In this paper, we propose an approach to efficiently solve the near neighbor searching problem. In this approach, along each dimension an index is constructed according to the values of feature points of multimedia objects. A user can pose a content-based query by specifying a multimedia query example and a similarity measure. The specified query example will be transformed into a query point in the multi-dimensional feature space. The possible result points in each dimension are then retrieved by searching the value of the query point in the corresponding dimension. The sets of the possible result points are merged one by one by removing the points which are not within the query radius. The resultant points and their distances from the query point form the answer of the query. To show the efficiency of our approach, a series of experiments are performed to compare with the related approaches.