Spatial Range Queries Research Articles

Linear separability in spatial databases

Given two spatial point sets R and B in the plane, with cardinalities m and n, respectively, and stored in two separate R-trees, we propose an efficient algorithm to verify whether R and B are linearly separable. The sets R and B are linearly separable if there exists a line that splits the plane into to halfplanes, one containing all R and the other one containing all B. This is the first algorithm that answers the separability question in the context of the spatial data bases. That is, it considers as input big spatial data stored in secondary storage data structures (e.g., the R-tree) which are not allowed to be completely stored in the main memory of the computer to run a classic algorithm. The algorithms designed in this context aim to minimize as much as possible the number of blocks read from the secondary storage data structures to the main memory. Studied problems in this setting are the k-nearest neighbor problem and the spatial range query problem. Our algorithm explicitly exploits the geometric and spatial properties of the R-trees to access only the nodes relevant to decide the linear separability of the given sets. Our experimental results show the efficiency of the algorithm, since it accesses between the 0.34 and 2.79% of the nodes of the R-trees. We also analyze the asymptotic running time of the algorithm, showing that it runs in $$O(m\log m + n\log n)$$ time in the worst case.

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications such as disaster response. However, the time-constraint analysis is not yet a trivial task in the cloud computing environment. Spatial query processing (SQP) is typical computation-intensive and indispensable for GBDA, and the spatial range query, join query, and the nearest neighbor query algorithms are not scalable without using MapReduce-liked frameworks. Parallel SQP algorithms (PSQPAs) are trapped in screw-processing, which is a known issue in Geoscience. To satisfy time-constrained GBDA, we propose an elastic SQP approach in this paper. First, Spark is used to implement PSQPAs. Second, Kubernetes-managed Core Operation System (CoreOS) clusters provide self-healing Docker containers for running Spark clusters in the cloud. Spark-based PSQPAs are submitted to Docker containers, where Spark master instances reside. Finally, the horizontal pod auto-scaler (HPA) would scale-out and scale-in Docker containers for supporting on-demand computing resources. Combined with an auto-scaling group of virtual instances, HPA helps to find each of the five nearest neighbors for 46,139,532 query objects from 834,158 spatial data objects in less than 300 s. The experiments conducted on an OpenStack cloud demonstrate that auto-scaling containers can satisfy time-constraint GBDA in clouds.

VIP-Tree

Due to the growing popularity of indoor location-based services, indoor data management has received significant research attention in the past few years. However, we observe that the existing indexing and query processing techniques for the indoor space do not fully exploit the properties of the indoor space. Consequently, they provide below par performance which makes them unsuitable for large indoor venues with high query workloads. In this paper, we propose two novel indexes called Indoor Partitioning Tree (IP-Tree) and Vivid IP-Tree (VIP-Tree) that are carefully designed by utilizing the properties of indoor venues. The proposed indexes are lightweight, have small pre-processing cost and provide near-optimal performance for shortest distance and shortest path queries. We also present efficient algorithms for other spatial queries such asknearest neighbors queries and range queries. Our extensive experimental study on real and synthetic data sets demonstrates that our proposed indexes outperform the existing algorithms by several orders of magnitude.

An Efficient Privacy-Preserving Location-Based Services Query Scheme in Outsourced Cloud

With the pervasiveness of location-aware mobile electronic devices and the advances of wireless communications, location-based services (LBS), which can help people enjoy a convenient life, has attracted considerable interest recently. However, the privacy issues of LBS are still challenging today. Aiming at the challenges, in this paper, we present a new efficient and privacy-preserving LBS query scheme in outsourced cloud, i.e., EPQ, for pervasive smartphones. In the EPQ scheme, the LBS provider's data are first outsourced to the cloud server in an encrypted manner, and then, a registered user can get accurate LBS query results without divulging his/her location information to the LBS provider and the cloud server. Specifically, based on an improved homomorphic encryption technique over a composite order group, a special spatial range query algorithm SRQC over ciphertext is proposed, with which EPQ achieves privacy preservation of user's query and confidentiality of LBS data in the outsourced cloud server. Through detailed security analysis, we show that EPQ can resist various known security threats. In addition, we also implement EPQ over a smartphone and three workstations with a real LBS data set, and extensive simulation results further demonstrate that the proposed EPQ scheme is highly efficient at the smartphone side and can be implemented effectively in the cloud server.

PROVIDING R-TREE SUPPORT FOR MONGODB

Abstract. Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

PROVIDING R-TREE SUPPORT FOR MONGODB

Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

EPLQ: Efficient Privacy-Preserving Location-Based Query Over Outsourced Encrypted Data

With the pervasiveness of smart phones, location-based services (LBS) have received considerable attention and become more popular and vital recently. However, the use of LBS also poses a potential threat to user's location privacy. In this paper, aiming at spatial range query, a popular LBS providing information about points of interest (POIs) within a given distance, we present an efficient and privacy-preserving location-based query solution, called EPLQ. Specifically, to achieve privacy-preserving spatial range query, we propose the first predicate-only encryption scheme for inner product range (IPRE), which can be used to detect whether a position is within a given circular area in a privacy-preserving way. To reduce query latency, we further design a privacy-preserving tree index structure in EPLQ. Detailed security analysis confirms the security properties of EPLQ. In addition, extensive experiments are conducted, and the results demonstrate that EPLQ is very efficient in privacy-preserving spatial range query over outsourced encrypted data. In particular, for a mobile LBS user using an Android phone, around 0.9 s is needed to generate a query, and it also only requires a commodity workstation, which plays the role of the cloud in our experiments, a few seconds to search POIs.

Compressed spatial hierarchical bitmap (cSHB) indexes for efficiently processing spatial range query workloads

In most spatial data management applications, objects are represented in terms of their coordinates in a 2-dimensional space and search queries in this space are processed using spatial index structures. On the other hand, bitmap-based indexing, especially thanks to the compression opportunities bitmaps provide, has been shown to be highly effective for query processing workloads including selection and aggregation operations. In this paper, we show that bitmap-based indexing can also be highly effective for managing spatial data sets. More specifically, we propose a novel compressed spatial hierarchical bitmap (cSHB) index structure to support spatial range queries. We consider query workloads involving multiple range queries over spatial data and introduce and consider the problem of bitmap selection for identifying the appropriate subset of the bitmap files for processing the given spatial range query workload. We develop cost models for compressed domain range query processing and present query planning algorithms that not only select index nodes for query processing, but also associate appropriate bitwise logical operations to identify the data objects satisfying the range queries in the given workload. Experiment results confirm the efficiency and effectiveness of the proposed compressed spatial hierarchical bitmap (cSHB) index structure and the range query planning algorithms in supporting spatial range query workloads.

Single and Multiple point Spatial Queries Supporting Keywords for Searching Nearest Neighbors

Today's applications request for finding spatial objects closest to a specified location or within some range which satisfy constraint of keywords. Initially, spatial queries finding nearest neighbor or range queries having conditions on only geometric properties of object points. For example, in the emergency of accident taking all hospital in consideration is not useful rather than finding hospital having facilities like ICU and emergency facilities at the same time. Currently, solution to these queries is based on the IR2-tree is not capable to provide real time effective answers. A new method named the SI-index Spatial Inverted) is developed extends the capabilities of conventional inverted index manages with multidimensional data, along with the solution of moving range queries answered by using SI-index results to algorithm which solves the problem in real time.

Search continuous spatial keyword range queries over moving objects in road networks

Search continuous spatial keyword range queries over moving objects in road networks

A taxonomy for region queries in spatial databases

In spatial databases, there are two basic types of queries, namely nearest neighbour queries (kNN) and range queries. Spatial range queries are not only finding objects of interest within a certain range or radius, but feature a wide spectrum: from finding objects of interest to forming the range (or region). Therefore, in this paper, we coin a term “Region Queries” to indicate a broad category of spatial range queries. It is imperative to understand the full capabilities of region queries, before starting to work on processing and optimising such queries. The aim of this paper is to show a complete picture of region queries. In this study, we present taxonomy of region queries, comprising of three categories: (i) finding objects of interest, (ii) forming regions, and (iii) determining centroids. These three query types form a comprehensive view of what region queries are about.

Efficient Processing of Spatial Range Queries on Wireless Broadcast Streams

Efficient Processing of Spatial Range Queries on Wireless Broadcast Streams

Efficient Processing of Spatial Range Queries on Wireless Broadcast Streams

With advances in wireless networks and hand-held computing devices equipped with location sensing capability (e.g., PDAs, laptops, and smart phones), a large number of location based services (LBSs) have been successfully deployed. In LBSs, wireless broadcast is an efficient method to support the large number of users. In wireless broadcast environment, existing research proposed to support range queries search, may tune into unnecessary indexes or data object. This paper addresses the problem of processing range queries on wireless broadcast streams. In order to support range queries efficiently, we propose a novel indexing scheme called Distributed Space-Partitioning Index (DSPI). DSPI consists of hierarchical grids that provide mobile clients with the global view as well as the local view of the broadcast data. The algorithm for processing range queries based on DSPI is also proposed. Simulation experiments demonstrate DSPI is superior to the existing index schemes.

A Range Query Parallel Algorithm in High-Dimensional Space

The performance of the spatial range query algorithms based on Brute-Force method, R-tree, VA-file and NB-tree suffers greatly in high-dimensional space. So the reduction of the dimensionality is the key to the spatial range query in high-dimensional space. The paper uses the parallel technique to present a spatial range query parallel algorithm in high-dimensional space. The algorithm transforms d-dimensional spatial range query to the linear space on d slave node processors. The d slave node processors run parallel. The master node processor only need calculate the union of d results which d slave node processors return. The experimental results indicate that its performance is better than that of the spatial range query algorithms based on Brute-Force method, R-tree, VA-file, NB-tree.

Approximate range query processing in spatial network databases

Spatial range query is one of the most common queries in spatial databases, where a user invokes a query to find all the surrounding interest objects. Most studies in range search consider Euclidea...

A VOMR-TREE BASED PARALLEL RANGE QUERY METHOD ON DISTRIBUTED SPATIAL DATABASE

Abstract. Spatial index impacts upon the efficiency of spatial query seriously in distributed spatial database. In this paper, we introduce a parallel spatial range query algorithm, based on VoMR-tree index, which incorporates Voronoi diagrams into MR-tree, benefiting from the nearest neighbors. We first augments MR-tree to store the nearest neighbors and constructs the VoMR-tree index by Voronoi diagram. We then propose a novel range query algorithm based on VoMR-tree index. In processing a range query, we discuss the data partition method so that we can improve the efficiency by parallelization in distributed database. Just then a verification strategy is promoted. We show the superiority of the proposed method by extensive experiments using data sets of various sizes. The experimental results reveal that the proposed method improves the performance of range query processing up to three times in comparison with the widely-used R-tree variants.

Equivalent Disk Allocations

Declustering techniques reduce query response times through parallel I/O by distributing data among multiple devices. Except for a few cases, it is not possible to find declustering schemes that are optimal for all spatial range queries. As a result of this, most of the research on declustering have focused on finding schemes with low worst case additive error. Number-theoretic declustering techniques provide low additive error and high threshold. In this paper, we investigate equivalent disk allocations and focus on number-theoretic declustering. Most of the number-theoretic disk allocations are equivalent and provide the same additive error and threshold. Investigation of equivalent allocations simplifies schemes to find allocations with desirable properties. By keeping one of the equivalent disk allocations, we can reduce the complexity of searching for good disk allocations under various criteria such as additive error and threshold. Using proposed scheme, we were able to collect the most extensive experimental results on additive error and threshold in 2, 3, and 4 dimensions.

Context-aware and quality-aware algorithms for efficient mobile object management

The management of positions of mobile objects is an essential prerequisite for many context-aware systems such as advanced traffic management systems or personal assistance systems. In this paper, we present two approaches for the scalable tracking of mobile object trajectories and the efficient processing of continuous spatial range queries, respectively. We show in detail how both approaches utilize the basic concepts of accuracy relaxation and utilization of context information, such as movement predictions, to minimize the number of position updates, the size of trajectory data, and the number of energy-consuming position sensing operations.

Divide-and-conquer scheme for strictly optimal retrieval of range queries

Declustering distributes data among parallel disks to reduce retrieval cost using I/O parallelism. Many schemes were proposed for single copy declustering of spatial data. Recently, declustering using replication gained a lot of interest and several schemes with different properties were proposed. It is computationally expensive to verify optimality of replication schemes designed for range queries and existing schemes verify optimality for up to 50 disks. In this article, we propose a novel method to find replicated declustering schemes that render all spatial range queries optimal. The proposed scheme uses threshold based declustering, divisibility of large queries for optimization and optimistic approach to compute maximum flow. The proposed scheme is generic and works for any number of dimensions. Experimental results show that using 3 copies there exist allocations that render all spatial range queries optimal for up to 750 disks in 2 dimensions and with the exception of several values for up to 100 disks in 3 dimensions. The proposed scheme improves search for strictly optimal replicated declustering schemes significantly and will be a valuable tool to answer open problems on replicated declustering.

Web data retrieval: solving spatial range queries using k-nearest neighbor searches

As Geographic Information Systems (GIS) technologies have evolved, more and more GIS applications and geospatial data are available on the web. Spatial objects in a given query range can be retrieved using spatial range query ? one of the most widely used query types in GIS and spatial databases. However, it can be challenging to retrieve these data from various web applications where access to the data is only possible through restrictive web interfaces that support certain types of queries. A typical scenario is the existence of numerous business web sites that provide their branch locations through a limited "nearest location" web interface. For example, a chain restaurant's web site such as McDonalds can be queried to find some of the closest locations of its branches to the user's home address. However, even though the site has the location data of all restaurants in, for example, the state of California, it is difficult to retrieve the entire data set efficiently due to its restrictive web interface. Considering that k-Nearest Neighbor (k-NN) search is one of the most popular web interfaces in accessing spatial data on the web, this paper investigates the problem of retrieving geospatial data from the web for a given spatial range query using only k-NN searches. Based on the classification of k-NN interfaces on the web, we propose a set of range query algorithms to completely cover the rectangular shape of the query range (completeness) while minimizing the number of k-NN searches as possible (efficiency). We evaluated the efficiency of the proposed algorithms through statistical analysis and empirical experiments using both synthetic and real data sets.