Efficient Spatial Query Processing Research Articles

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector data (SVD) with their changeable data schemas, agile scalability, and fast query response time. Spatial queries are basic operations in geospatial databases. According to Green information technology, an efficient spatial index can accelerate query processing and save power consumption for ubiquitous spatial applications. Current solutions tend to pursue it by indexing spatial objects with space-filling curves or geohash on NoSQL databases. As for the performance-wise R-tree family, they are mainly used in slow disk-based spatial access methods on NoSQL databases that incur high loading and searching costs. Therefore, performing spatial queries efficiently with the R-tree family on NoSQL databases remains a challenge. In this paper, an in-memory balanced and distributed R*-tree index named the BDRST index is proposed and implemented on HBase for efficient spatial-query processing of massive SVD. The BDRST index stores and distributes serialized R*-trees to HBase regions in association with SVD partitions in the same table. Moreover, an efficient optimized server-side parallel processing framework is presented for real-time R*-tree instantiation and query processing. Through extensive experiments on real-world land-use data sets, the performance of our method is tested, including index building, index quality, spatial queries, and applications. Our proposed method outperforms other state-of-the-art solutions, saving between 27.36% and 95.94% on average execution time for the above operations. Experimental results show the capability of the BDRST index to support spatial queries over large-scale SVD, and our method provides a solution for efficient sustainability research that involves massive georeferenced data.

Efficient processing of top-k frequent spatial keyword queries.

The rapid popularization of high-speed mobile communication technology and the continuous development of mobile network devices have given spatial textual big data (STBD) new dimensions due to their ability to record geographical objects from multiple sources and with complex attributes. Data mining from spatial textual datasets has become a meaningful study. As a popular topic for STBD, the top-k spatial keyword query has been developed in various forms to deal with different retrievals requirements. However, previous research focused mainly on indexing locational attributes and retrievals of few target attributes, and these correlations between large numbers of the textual attributes have not been fully studied and demonstrated. To further explore interrelated-knowledge in the textual attributes, this paper defines the top-k frequent spatial keyword query (tfSKQ) and proposes a novel hybrid index structure, named RCL-tree, based on the concept lattice theory. We also develop the tfSKQ algorithms to retrieve the most frequent and nearest spatial objects in STBD. One existing method and two baseline algorithms are implemented, and a series of experiments are carried out using real datasets to evaluate its performance. Results demonstrated the effectiveness and efficiency of the proposed RCL-tree in tfSKQ with the complex spatial multi keyword query conditions.

Efficient parallel processing of high-dimensional spatial kNN queries

Efficient parallel processing of high-dimensional spatial kNN queries

Hu-Fu

Data isolation has become an obstacle to scale up query processing over big data, since sharing raw data among data owners is often prohibitive due to security concerns. A promising solution is to perform secure queries over a federation of multiple data owners leveraging secure multi-party computation (SMC) techniques, as evidenced by recent federation work over relational data. However, existing solutions are highly inefficient on spatial queries due to excessive secure distance operations for query processing and their usage of general-purpose SMC libraries for secure operation implementation. In this paper, we propose Hu-Fu, the first system for efficient and secure spatial query processing on a data federation. The idea is to decompose the secure processing of a spatial query into as many plaintext operations and as few secure operations as possible, where fewer secure operators are involved and all secure operators are implemented dedicatedly. As a working system, Hu-Fu supports not only query input in native SQL, but also heterogeneous spatial databases ( e.g. , PostGIS, Simba, GeoMesa, and SpatialHadoop) at the backend. Extensive experiments show that Hu-Fu usually outperforms the state-of-the-arts in running time and communication cost while guaranteeing security.

GeoFlink: An Efficient and Scalable Spatial Data Stream Management System

This era is witnessing an exponential growth in spatial data due to the increase in GPS-enabled devices. Spatial data can be of extreme use to commercial businesses, governments and NGOs if processed timely. Spatial data is voluminous and is usually generated as a continuous data stream, for instance, vehicles tracking data, mobile location data, etc. To process such a huge data streams, highly scalable systems are needed. Apache Spark Streaming, Apache Flink, and Apache Samza are among the state-of-the-art scalable stream processing platforms; however, they lack spatial objects, indexes, and queries support. Besides them, other scalable spatial data processing platforms including GeoSpark, Spatial Hadoop do not support streaming workloads and can only handle static or batch data. To fill this gap, we present GeoFlink which extends Apache Flink to support spatial objects, indexes and continuous queries over spatial data streams. A grid-based index is introduced to support efficient spatial query processing and effective data distribution across distributed cluster nodes. GeoFlink supports spatial range, spatial <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN and spatial join queries on Point, LineString, Polygon, MultiPoint, MultiLineString, and MultiPolygon spatial objects. Besides, GeoFlink supports data streams in GeoJSON, WKT, and CSV data formats. A detailed experimental study on real and synthetic spatial data streams proves that GeoFlink achieves significantly higher query throughput than the existing state-of-the-art streaming platforms.

Facilitating Secure and Efficient Spatial Query Processing on the Cloud

Database outsourcing is a common cloud computing paradigm that allows data owners to take advantage of its on-demand storage and computational resources. The main challenge is maintaining data confidentiality with respect to untrusted parties i.e., cloud service provider, as well as providing relevant query results in real-time to authenticated users. Existing approaches either compromise confidentiality of the data or suffer from high communication cost between the server and the user. To overcome this problem, we propose a dual transformation and encryption scheme for spatial data, where encrypted queries are executed entirely at the service provider on the encrypted database and encrypted results are returned to the user. The user issues encrypted spatial range queries to the service provider and then uses the encryption key to decrypt the query response returned. This allows a balance between the security of data and efficient query response as the queries are processed on encrypted data at the cloud server. Moreover, we compare with existing approaches on large datasets and show that this approach reduces the average query communication cost between the authorized user and service provider, as only a single round of communication is required by the proposed approach.

Efficient Processing of Spatial Preference Queries in Spatial Network Databases

Efficient Processing of Spatial Preference Queries in Spatial Network Databases

An efficient approach for big data processing using spatial Boolean queries

The web is being used more and more by users of mobile devices. In addition, it is increasingly possible to track the user’s location, which provides immense opportunities in geospatial data and its management. Due to the use of location information in services for each mobile device, a large size of spatial data makes it difficult to process spatial queries efficiently and, therefore, we need a lightweight and scalable approach to process large amounts of stored data in distributed file systems. For the most part, all SNSs (social network services) focus on connecting the user account with their location information, such as check-in services, which helps them collect information about user activities and ratings. Of location, but also increases the load of data on their servers. . In this article we propose an indexing technique in combination with efficient processing of Boolean top-k spatial queries where location data is compressed to save space and the Boolean query helps filter results so that unrelated data is not processed, what helps to save space and faster processing of queries.

Efficient spatial query processing for KNN queries using well organised net-grid partition indexing approach

In recent years, most of the applications use mobile devices with geographical positioning systems support for providing location-based services. However, the queries sent through the mobile devices to obtain such services consume more time for processing due to the size of the spatial data. In order to solve this problem, an efficient indexing method for providing effective query processing services in mobile computing environments is proposed. This indexing method increases the efficiency of the query retrieval in mobile network environments. Since, all the existing mobile-based network applications utilise the node to node access of spatial objects for processing the query, the mobile query retrieval part in spatial databases is becoming the greatest disadvantage by consuming more time to process the query. The experimental results carried out using the proposed net-grid-based partition index approach show that the proposed model provides fast retrieval with high accuracy in processing of spatial queries.

Efficient spatial query processing for KNN queries using well organised net-grid partition indexing approach

In recent years, most of the applications use mobile devices with geographical positioning systems support for providing location-based services. However, the queries sent through the mobile devices to obtain such services consume more time for processing due to the size of the spatial data. In order to solve this problem, an efficient indexing method for providing effective query processing services in mobile computing environments is proposed. This indexing method increases the efficiency of the query retrieval in mobile network environments. Since, all the existing mobile-based network applications utilise the node to node access of spatial objects for processing the query, the mobile query retrieval part in spatial databases is becoming the greatest disadvantage by consuming more time to process the query. The experimental results carried out using the proposed net-grid-based partition index approach show that the proposed model provides fast retrieval with high accuracy in processing of spatial queries.

Efficient large-scale distance-based join queries in spatialhadoop

Efficient processing of Distance-Based Join Queries (DBJQs) in spatial databases is of paramount importance in many application domains. The most representative and known DBJQs are the K Closest Pairs Query (KCPQ) and the e Distance Join Query (eDJQ). These types of join queries are characterized by a number of desired pairs (K) or a distance threshold (e) between the components of the pairs in the final result, over two spatial datasets. Both are expensive operations, since two spatial datasets are combined with additional constraints. Given the increasing volume of spatial data originating from multiple sources and stored in distributed servers, it is not always efficient to perform DBJQs on a centralized server. For this reason, this paper addresses the problem of computing DBJQs on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports efficient processing of spatial queries in a cloud-based setting. We propose novel algorithms, based on plane-sweep, to perform efficient parallel DBJQs on large-scale spatial datasets in SpatialHadoop. We evaluate the performance of the proposed algorithms in several situations with large real-world as well as synthetic datasets. The experiments demonstrate the efficiency and scalability of our proposed methodologies.

Efficient Spatial Keyword Query Processing in the Internet of Industrial Vehicles

With the development of the Internet of Things (IoT), the industrial vehicle ad hoc networks are revolving into the Internet of Industrial Vehicles (IoIV). Due to the popularity of the geographical devices used on the Industrial vehicle, location-based information is extensively available in IoIV. This development calls for spatial keyword queries (SKQ), which takes into account both the locations and textual descriptions of objects. This paper addresses the issue of processing SKQ in IoIV environment, which focuses on two types of SKQ queries, namely Boolean kNN Queries and Top-k Queries. A general air index called Extended Spatial Keyword query index in IoIV environment (ESKIV) is proposed, which supports both network space pruning and textual pruning simultaneously. Based on ESKIV, efficient algorithms are designed to deal with these two types of SKQ respectively. The proposed ESKIV also can be used to deal with other kinds of queries, such as range SKQ. Finally, extensive simulations are conducted to demonstrate the efficiency of our ESKIV index and the corresponding query processing algorithms.

Editorial for the special issue on heterogeneous sensors–based object identification and information fusion

With the fast development of computing, communication, control technologies, the proliferation of heterogeneous sensors with data communication capability can provide a wide variety of data for our daily life, such as road monitoring, health-care systems, structure health checking, military applications etc. However, there are still many interesting open research problems left to be explored as well as many issues to be addressed. In this special issue, we plan to focus on various research and application issues in heterogeneous sensors based object identification and information fusion. The first paper, 'A novel distributed air index for efficient spatial query processing in road sensor networks on the air' by Yanhong Li et al., explores the problem of spatial query processing in road sensor networks by means of wireless data broadcast. In addition it presents an efficient method to partition the record-keeping information about the underlying road sensor network and its associated objects, by which develops a fully distributed air index, called integrated exponential index, based on an extended version of the Hilbert curve. Besides, this paper also proposes efficient client-side algorithms to facilitate the processing of several kinds of spatial queries, including kNN query, CkNN query, and range query. The problem of enforcing the integrity of the outsourced data remotely is addressed in the paper, 'Parallel checking of content integrity in multi-cloud storage of heterogeneous sensor systems' by Jian Mao et al.. They propose a parallel cloud data possession checking scheme for the multi-cloud environment, which utilizes the homomorphic verification tag created by the Paillier cryptosystem to support unlimited query challenges and introduces the error-correction encoding method to ensure error localization and data correction. The paper, 'Subscribing to fuzzy temporal aggregation of heterogeneous sensor streams in real-time distributed environments' by J. Medina et al., presents an approach for distributing and processing heterogeneous data based on a representation with fuzzy linguistic terms, to solve the problems of the data fusion of sensors and the design of processing information. What's more, in order to illustrate the usefulness and effectiveness of this proposal, the authors present the results of the fuzzy temporal aggregation of sensor streams with alpha-cut subscriptions in a case study where an

Exploiting location-aware social networks for efficient spatial query processing

In this paper, we introduce two watchtower-based parameter-tunable frameworks for efficient spatial processing with sparse distributions of Points of Interest (POIs) by exploiting mobile users' check-in data collected from the location-aware social networks. In our proposed frameworks, the network traversal can terminate earlier by retrieving the distance information stored in watchtowers. More important, by observing that people's movement often exhibits a strong spatial pattern, we employ Bayesian Information Criterion-based cluster analysis to model mobile users' check-in data as a mixture of 2-dimensional Gaussian distributions, where each cluster corresponds to a geographical hot zone. Afterwards, POI watchtowers are established in the hot zones and non-hot zones discriminatorily. Moreover, we discuss the optimal watchtower deployment mechanism in order to achieve a desired balance between the off-line pre-computation cost and the on-line query efficiency. Finally, the superiority of our solutions over the state-of-the-art approaches is demonstrated using the real data collected from Gowalla with large-scale road networks.

A novel distributed air index for efficient spatial query processing in road sensor networks on the air

SummaryRecent development of wireless technology and smart mobile devices has spurred intense research efforts to address spatial queries. In particular, an increasing interest for tackling spatial query processing in broadcast environments has been observed. To the best of our knowledge, most of the existing work on this problem has assumed an Euclidean space. However, for real applications, query clients move within a road sensor network, where the distance between a data object and a query is determined by the connectivity of the road sensor network. This paper explores the problem of spatial query processing in road sensor networks by means of wireless data broadcast. We present an efficient method to partition the record‐keeping information about the underlying road sensor network and its associated objects, by which we develop a fully distributed air index, called integrated exponential index, based on an extended version of the Hilbert curve. We also propose efficient client‐side algorithms to facilitate the processing of several kinds of spatial queries, including kNN query, CkNN query, and range query. Finally, extensive simulation experiments have been conducted to demonstrate the strengths of our proposed techniques. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Efficient Collective Spatial Keyword Query Processing on Road Networks

The collective spatial keyword query (CSKQ), an important variant of spatial keyword queries, aims to find a set of the objects that collectively cover users' queried keywords, and those objects are close to the query location and have small inter-object distances. Existing works only focus on the CSKQ problem in the Euclidean space, although we observe that, in many real-life applications, the closeness of two spatial objects is measured by their road network distance. Thus, existing methods cannot solve the problem of network-based CSKQ efficiently. In this paper, we study the problem of collective spatial keyword query processing on road networks , where the objects are located on a predefined road network. We first prove that this problem is NP-complete , and then we propose two approximate algorithms with provable approximation bounds and one exact algorithm, for supporting CSKQ on road networks efficiently. Extensive experiments using real datasets demonstrate the efficiency and accuracy of our presented algorithms.

An efficient spatial query processing algorithm in multi-sink wireless sensor networks

In order to address the problem of energy and time-efficient execution of spatial queries in multi-sink wireless sensor networks, an efficient hybrid spatial query processing algorithm (EHSQP) is proposed in this paper. In EHSQP, sink nodes perform the sector selection for the query region and seek the query route to a query region using the known infrastructure information. Sensor nodes can adjust the query dissemination and reversing route for query results based on the available local information. To minimise the energy consumption and the response time, the proposed EHSQP algorithm ensures that only the relevant nodes are involved in the query execution. Experimental results show that the proposed algorithm reduces communication cost significantly, and saves energy and time very effectively for the connected sensors in the given region. The proposed technique has an advantage over other techniques in terms of energy and time-efficient query cover with lower communication cost.

An efficient spatial query processing algorithm in multi-sink wireless sensor networks

In order to address the problem of energy and time-efficient execution of spatial queries in multi-sink wireless sensor networks, an efficient hybrid spatial query processing algorithm EHSQP is proposed in this paper. In EHSQP, sink nodes perform the sector selection for the query region and seek the query route to a query region using the known infrastructure information. Sensor nodes can adjust the query dissemination and reversing route for query results based on the available local information. To minimise the energy consumption and the response time, the proposed EHSQP algorithm ensures that only the relevant nodes are involved in the query execution. Experimental results show that the proposed algorithm reduces communication cost significantly, and saves energy and time very effectively for the connected sensors in the given region. The proposed technique has an advantage over other techniques in terms of energy and time-efficient query cover with lower communication cost.

SparkGIS: Efficient Comparison and Evaluation of Algorithm Results in Tissue Image Analysis Studies.

Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison of multiple results, and facilitate algorithm sensitivity studies. The sizes of images and analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present SparkGIS, a distributed, in-memory spatial data processing framework to query, retrieve, and compare large volumes of analytical image result data for algorithm evaluation. Our approach combines the in-memory distributed processing capabilities of Apache Spark and the efficient spatial query processing of Hadoop-GIS. The experimental evaluation of SparkGIS for heatmap computations used to compare nucleus segmentation results from multiple images and analysis runs shows that SparkGIS is efficient and scalable, enabling algorithm evaluation and algorithm sensitivity studies on large datasets.

Efficient Processing of Spatial Group Keyword Queries

With the proliferation of geo-positioning and geo-tagging techniques, spatio-textual objects that possess both a geographical location and a textual description are gaining in prevalence, and spatial keyword queries that exploit both location and textual description are gaining in prominence. However, the queries studied so far generally focus on finding individual objects that each satisfy a query rather than finding groups of objects where the objects in a group together satisfy a query. We define the problem of retrieving a group of spatio-textual objects such that the group's keywords cover the query's keywords and such that the objects are nearest to the query location and have the smallest inter-object distances. Specifically, we study three instantiations of this problem, all of which are NP-hard. We devise exact solutions as well as approximate solutions with provable approximation bounds to the problems. In addition, we solve the problems of retrieving top- k groups of three instantiations, and study a weighted version of the problem that incorporates object weights. We present empirical studies that offer insight into the efficiency of the solutions, as well as the accuracy of the approximate solutions.