Given a point q, a reverse k nearest neighbor (RkNN) query retrieves all the data points that have q as one of their k nearest neighbors. Existing methods for processing such queries have at least one of the following they are applicable only to 2D data (but not to higher dimensionality), and (iv) they retrieve only approximate results. Motivated by these shortcomings, we develop algorithms for exact processing of RkNN with arbitrary values of k on dynamic multidimensional datasets. Our methods utilize a conventional data-partitioning index on the dataset and do not require any pre-computation. In addition to their flexibility, we experimentally verify that the proposed algorithms outperform the existing ones even in their restricted focus.

Given two spatial datasets P (e.g., facilities) and Q (queries), an aggregate nearest neighbor (ANN) query retrieves the point(s) of P with the smallest aggregate distance(s) to points in Q. Assuming, for example, n users at locations q1,... qn,anANN query outputs the facility p ∈ P that minimizes the sum of distances |pqi | for 1 ≤ i ≤ n that the users have to travel in order to meet there. Similarly, another ANN query may report the point p ∈ P that minimizes the maximum distance that any user has to travel, or the minimum distance from some user to his/her closest facility. If Q fits in memory and P is indexed by an R-tree, we develop algorithms for aggregate nearest neighbors that capture several versions of the problem, including weighted queries and incremental reporting of results. Then, we analyze their performance and propose cost models for query optimization. Finally, we extend our techniques for disk-resident queries and approximate ANN retrieval. The efficiency of the algorithms and the accuracy of the cost models are evaluated through extensive experiments with real and synthetic datasets.

Abstract not found

More and more applications require peer-to-peer (P2P) systems to support complex queries over multi-dimensional data. For example, a P2P auction network for real estate frequently needs to answer queries such as “select five available buildings closest to the airport”. Such queries are not efficiently supported in current P2P systems. Towards an efficient and scalable P2P system capable of processing complex multi-dimensional queries, we first propose a comprehensive framework for sharing, indexing, and querying multi-dimensional data, where (i) peers with more computational power coordinate indexing and query processing, and (ii) other peers participate in part of the computation in order to achieve scalability and loadbalance. Based on this framework, we propose Network-Rtree (NR-tree), a P2P adaptation of the dominant spatial index- R*-tree. NR-tree, indexing spatial data at clustered peers, is capable of processing complex queries such as range queries and k-nearest neighbor queries. We propose query processing algorithms for range and k-nearest neighbor queries and experimentally prove the effectiveness of proposed techniques with real data. 1.

Abstract. With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services. In this paper, we investigate mechanisms to perform NN search on R-tree-like structures storing historical information about moving object trajectories. The proposed branch-and-bound algorithms vary with respect to the type of the query object (stationary or moving point) as well as the type of the query result (continuous or not). We also propose novel metrics to support our search ordering and pruning strategies. Using the implementation of the proposed algorithms on a member of the R-tree family for trajectory data (the TBtree), we demonstrate their scalability and efficiency through an extensive experimental study using synthetic and real datasets. 1

This paper addresses the problem of finding the K closest pairs between two spatial datasets (the socalled, K closest pairs query, K-CPQ), where each dataset is stored in an R-tree. There are two di#erent techniques for solving this kind of distance-based query. The first technique is the incremental approach, which returns the output elements one-by-one in ascending order of distance. The second one is the nonincremental alternative, which returns the K elements of the result all together at the end of the algorithm. In this paper, based on distance functions between two MBRs in the multidimensional Euclidean space, we propose a pruning heuristic and two updating strategies for minimizing the pruning distance, and use them in the design of three non-incremental branch-and-bound algorithms for K-CPQ between spatial objects stored in two R-trees. Two of those approaches are recursive following a Depth-First searching strategy and one is iterative obeying a Best-First traversal policy. The plane-sweep method and the search ordering are used as optimization techniques for improving the naive approaches. Besides, a number of interesting extensions of the K-CPQ (K-Self-CPQ, Semi-CPQ, K-FPQ (the K-farthest pairs query), etc.) are discussed. An extensive performance study is also presented. This study is based on experiments performed with real datasets. A wide range of values for the basic parameters a#ecting the performance of the algorithms is examined in order to designate the most e#cient algorithm for each setting of parameter values. Finally, an experimental study of the behavior of the proposed K-CPQ branch-and-bound algorithms in terms of scalability of the dataset size and the K value is also included.

Abstract. A location-based service (LBS) provides information based on the location information specified in a query. Nearest-neighbor (NN) search is an important class of queries supported in LBSs. This paper studies energy-conserving air indexes for NN search in a wireless broadcast environment. Linear access requirement of wireless broadcast weakens the performance of existing search algorithms designed for traditional spatial database. In this paper, we propose a new energyconserving index, called grid-partition index, which enables a single linear scan of the index for any NN queries. The idea is to partition the search space for NN queries into grid cells and index all the objects that are potential nearest neighbors of a query point in each grid cell. Three grid partition schemes are proposed for the grid-partition index. Performance of the proposed grid-partition indexes and two representative traditional indexes (enhanced for wireless broadcast) is evaluated using both synthetic and real data. The result shows that the grid-partition index substantially outperforms the traditional indexes.

With the advent of wireless networking and personal digital devices, the population of mobile users will increase significantly. Broadcasting is particularly suitable for environments having a large number of clients. In this paper, we study the query processing of some typical location-dependent queries, such as window queries and kNN queries, in a broadcast system. To reduce clients' power consumption and provide efficient services, a transformation of the objects is applied based on Hilbert Curve. Furthermore, a linear index structure is constructed and several algorithms are devised to answer spatial queries. Experiments are conducted to evaluate the performance of the proposed transformation and related algorithms. Results show that the proposed schemes outperform existing algorithms significantly.

The high bandwidth and low latency of the modern internet has made possible the deployment of distributed computing platforms. The XenoServer platform provides a distributed computing platform open to all and presents three major new challenges for resource discovery: Firstly, network location is key for effectively provisioning services, to mitigate against high-latency, high-load or component failure. Secondly, many services require a presence on several servers, with inter-related requirements. Finally, as the platform is open with respect to users and servers, large numbers of queries and updates are expected. To address these requirements we introduce and evaluate Xeno-Search, a new distributed service for selecting the machines to host components of multi-node distributed systems and which is uniquely able to express and efficiently answer complex queries with inter-related location constraints. We demonstrate that Xeno-Search represents a trade-off between accuracy and query time which avoids exhaustive search and supports multiple resources. In addition the performance of the algorithm and the quality of its server selections is investigated and the performance of the distributed service shown to be invariant as the number of nodes or items indexed increases.

A Reverse k-Nearest-Neighbor (RkNN) query finds the objects that take the query object as one of their k nearest neighbors. In this paper we propose new solutions for evaluating RkNN queries and its variant bichromatic RkNN queries on 2-dimensional location data. We present an algorithm named INCH that can compute a RkNN query’s search region (from which the query result candidates are drawn). In our RkNN evaluation algorithm called FINCH, the search region restricts the search space, and the search region is tightened each time a new result candidate is found. We also propose a method that enables us to apply any RkNN algorithm on bichromatic RkNN queries. With that, our FINCH algorithm is also used to evaluate bichromatic RkNN queries. Experiments show that our solutions are more efficient than existing algorithms.