Spatial Query Processing Research Articles

Secure Spatial Top-k Query Processing via Untrusted Location-Based Service Providers

This paper considers a novel distributed system for collaborative location-based information generation and sharing which become increasingly popular due to the explosive growth of Internet-capable and location-aware mobile devices. The system consists of a data collector, data contributors, location-based service providers (LBSPs), and system users. The data collector gathers reviews about points-of-interest (POIs) from data contributors, while LBSPs purchase POI data sets from the data collector and allow users to perform spatial top- $k$ queries which ask for the POIs in a certain region and with the highest $k$ ratings for an interested POI attribute. In practice, LBSPs are untrusted and may return fake query results for various bad motives, e.g., in favor of POIs willing to pay. This paper presents three novel schemes for users to detect fake spatial snapshot and moving top- $k$ query results as an effort to foster the practical deployment and use of the proposed system. The efficacy and efficiency of our schemes are thoroughly analyzed and evaluated.

An Edge-Based Algorithm for Spatial Query Processing in Real-Life Road Networks

An Edge-Based Algorithm for Spatial Query Processing in Real-Life Road Networks

A Caching Strategy for Spatial Queries in Mobile Networks

As a result of the recent developments in mobile terminals such as smartphones and laptop computers, as well as in wireless communication technologies such as GPS, location- based services are coming closer and closer to our daily life. However, the problems associated with the limited resources-such as limited wireless bandwidth, limited battery life, and small memory space-of mobile terminals remain unsolved. Research on cache utilization in mobile terminals, the development of efficient query processing algorithms, and improvement of the index structure in order to support efficient location-based service is geared towards alleviating these problems. In this paper, we propose location- based cache maintenance strategies for wireless broadcast environments in which a mobile client prefetches data that are expected to be used in the near future, and caches and maintains the data at a location close to the client's location. We also propose a hierarchical tree-based privacy approach for supporting anonymous location-based queries in wireless mobile data delivery systems. The results of experiments conducted using our proposed algorithms indicate that they help to reduce communication costs and support rapid spatial query processing.

A distributed air index based on maximum boundary rectangle over grid-cells for wireless non-flat spatial data broadcast.

In the pervasive computing environment using smart devices equipped with various sensors, a wireless data broadcasting system for spatial data items is a natural way to efficiently provide a location dependent information service, regardless of the number of clients. A non-flat wireless broadcast system can support the clients in accessing quickly their preferred data items by disseminating the preferred data items more frequently than regular data on the wireless channel. To efficiently support the processing of spatial window queries in a non-flat wireless data broadcasting system, we propose a distributed air index based on a maximum boundary rectangle (MaxBR) over grid-cells (abbreviated DAIM), which uses MaxBRs for filtering out hot data items on the wireless channel. Unlike the existing index that repeats regular data items in close proximity to hot items at same frequency as hot data items in a broadcast cycle, DAIM makes it possible to repeat only hot data items in a cycle and reduces the length of the broadcast cycle. Consequently, DAIM helps the clients access the desired items quickly, improves the access time, and reduces energy consumption. In addition, a MaxBR helps the clients decide whether they have to access regular data items or not. Simulation studies show the proposed DAIM outperforms existing schemes with respect to the access time and energy consumption.

The iOS/Android application “e-SoilMap” for creating a new user-oriented soil map

To enhance the efficiency of soil information in the field survey, we developed the mobile application called “e-SoilMap” for creating a new user-oriented soil map. This application has the following three unique functions: annotatable map, data sharing over the cloud and offline support for allowing the user to create a high-value-added soil map in their Internet space. Both iOS and Android versions of the mobile application mainly consist of three key components: (1) a map manager, (2) a soil manager and (3) a user manager. The map manager is responsible for displaying background maps to the user. The soil manager displays a soil map and related soil information, which are provided by the National Institute for Agro-Environmental Sciences (NIAES), on the background maps. And the user manager is responsible for creating an annotatable map and sharing the user data in the cloud. The “e-SoilMap” provides soil information to users using location-based spatial query processing and allows the creation of annotatable pins on the soil map. The main target of this application was designed to be a wide variety of technical fields such as agronomic assessment, engineering applications, hydrology and hydrogeologic assessments, environmental assessment and policy decisions.

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.

Adaptive spatial window aggregation query processing algorithm in wireless sensor networks

Users of wireless sensor networks (WSNs) often submit spatial window aggregation queries to obtain the summary information about a local area in the monitored region. We propose an adaptive spatial window aggregation query processing algorithm called ASWAP in this paper. It divides the query area into several disjoint sub–regions. A cluster head node is elected in each sub–region which collects the sensory data of sensor nodes in it, uses the data collected to calculate the partial query result and sends it to the cluster head node of the next sub–region. While a cluster head node fails during the above process, a recovery area can be found in its corresponding sub–region in which any node can substitute the cluster head node to resume the process of query processing. Experimental results show that in most cases ASWAP outperforms the existing IWQE algorithm in terms of energy consumption and query success rate.

Efficient processing of optimal meeting point queries in Euclidean space and road networks

Finding an optimal meeting point (OMP) for a group of people (or a set of objects) at different locations is an important problem in spatial query processing. There are many real-life applications related to this problem, such as determining the location of a conference venue, deciding the pick-up location of a tourist bus, and planing tactics of artificial intelligence in real-time strategy games. Formally, given a set $$Q$$ Q of query points in a spatial setting $$P$$ P , an OMP query fetches the point $$o\in P$$ o ? P that minimizes a cost function defined over the distances from $$o$$ o to all points in $$Q$$ Q . Since there are infinitely many locations in a given space setting, it is infeasible to examine all of them to find the OMP, and thus, the problem is challenging. In this paper, we study OMP queries in the following two spatial settings which are common in real-life applications: Euclidean space and road networks. In the setting of Euclidean space, we propose a general framework for answering all OMP query variants and also identify the best algorithms for particular types of OMP queries in the literature. In the setting of road networks, we study how to access only part of the road network and examine part of the candidates. Specifically, we explore two pruning techniques, namely Euclidean distance bound and threshold algorithm, which help improve the efficiency of OMP query processing. Extensive experiments are conducted to demonstrate the efficiency of our proposed approaches on both real and synthetic datasets.

ALPS: an efficient algorithm for top-k spatial preference search in road networks

In this paper, we study the processing of top-k spatial preference queries in road networks. A top-k spatial preference query retrieves a ranked list of the k best data objects based on the scores (e.g., qualities) of feature objects in their spatial neighborhoods. Several solutions have been proposed for top-k spatial preference queries in Euclidean space. However, far too little attention has been paid to top-k spatial preference queries in road networks, where the distance between two points is defined by the length of the shortest path connecting them. A simple way to answer top-k spatial preference queries is to examine the scores of feature objects in the proximity of each data object before returning a ranked list of the k best data objects. However, this simple method causes intolerable computation delays, thus rendering online processing inapplicable. Therefore, in this paper, we address this problem by presenting a new algorithm, called ALPS, for top-k spatial preference searches in road networks. Our experimental results demonstrate the superiority and effectiveness of ALPS for a wide range of problem settings.

Location-based grid-index for spatial query processing

Researchers have studied the various index structures and query processing algorithms to support efficient spatial queries in wireless communication environments. However, most previous studies have focused on server-side methods. In a server-side method, the server executes queries from individual clients and then relays the results. In our proposed method, the server transmits an index called DGI (Distributed Grid Index), and the client examines the received index to process spatial queries. The proposed index structure and search algorithm support efficient spatial queries in a wireless broadcast environment, shortening query search times.

Hadoop GIS

Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous positioning technologies, development of high resolution imaging technologies, and contribution from a large number of community users. There are two major challenges for managing and querying massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. In this paper, we present Hadoop-GIS - a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through spatial partitioning, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. Hadoop-GIS utilizes global partition indexing and customizable on demand local spatial indexing to achieve efficient query processing. Hadoop-GIS is integrated into Hive to support declarative spatial queries with an integrated architecture. Our experiments have demonstrated the high efficiency of Hadoop-GIS on query response and high scalability to run on commodity clusters. Our comparative experiments have showed that performance of Hadoop-GIS is on par with parallel SDBMS and outperforms SDBMS for compute-intensive queries. Hadoop-GIS is available as a set of library for processing spatial queries, and as an integrated software package in Hive.

지오센서 네트워크의 다중 공간질의 최적화를 위한 공간질의처리비용 예측 알고리즘 연구

최근 IoT (Internet of Things) 기술의 발전과 더불어 무선 환경에서 특정 영역에 위치하는 센서노드의 위치-센서정보를 에너지 효율적으로 수집하는 센서 네트워크 기반 공간질의처리에 대한 관심이 크게 증가하고 있다. 그리하여 센서노드에서 공간 필터링을 직접 수행하여 센서노드들 간의 통신 횟수를 감소시켜 에너지 소모를 최소화하는 다양한 공간질의처리 알고리즘 및 분산 공간색인방법들이 제안되어 왔다. 그러나 단일 공간질의처리 최적화에 중점을 두었던 기존 공간색인방법 및 알고리즘들은 IoT 환경에서 다수 사용자에 의하여 요청되는 다중 공간질의를 최적화하여 수행하기에는 한계가 있었다. 이에 본 논문에서는 센서 네트워크에서 다중 공간질의를 에너지 효율적으로 처리할 수 있는 최적화 알고리즘을 제안하고 있다. 제안된 다중 공간질의 최적화 알고리즘은 인접 영역에 주어지는 공간질의들을 통합하여 수행하는 '질의통합' 개념을 기본으로 하고 있다. 최적화 과정에서 질의들의 통합 또는 개별 수행에 대한 판단은 각 수행비용을 예측하여 결정하며, 본 논문에서는 질의처리 비용 예측 방법을 추가적으로 제안하고 있다. 끝으로, 성능평가에서는 GR-tree, SPIX, CPS의 공간색인방법에 대한 비교 실험을 통하여 제안된 알고리즘의 성능 분석결과를 제시하고 있다. W ith the recent advancement of IoT (Internet of Things) technology, there has been much interest in the spatial query processing which energy-efficiently acquires sensor readings from sensor nodes inside specified geographical area of interests. Therefore, various kinds of spatial query processing algorithms and distributed spatial indexing methods have been proposed. They can minimize energy consumption of sensor nodes by reducing wireless communication among them using in-network spatial filtering technology. However, they cannot optimize multiple spatial queries which w ill be w idely used in IoT, because most of them have focused on a single spatial query optimization. Therefore, we propose a new multiple spatial query optimization algorithm which can energy-efficiently process multiple spatial queries in a sensor network. The algorithm uses a concept of 'query merging' that performs the merged set after merging multiple spatial queries located at adjacent area. Here, our algorithm makes a decision on which is better between the merged and the separate execution of queries. For such the decision making, we additionally propose the cost estimation method on the spatial query execution. Finally, we analyze and clarify our algorithm's distinguished features using the spatial indexing methods of GR-tree, SPIX, CPS.

Geo-Store: A Framework for Supporting Semantics-Enabled Location-Based Services

Location-based services (LBSs) are popular information services for mobile users, who access them using location-aware mobile devices over wireless networks. To enable devices to understand and exchange information on the Web, LBS providers use Semantic Web data sources to develop semantics-enabled LBSs. However, there are currently limited solutions for processing spatial queries, the building blocks of most LBSs, on Semantic Web data to support these services. The Geo-Store system evaluates spatial queries on RDF triple stores (Semantic Web data management systems) to provide semantics-enabled LBSs.

Location based Spatial Position Query Processing Environment for Position Queries using Energy Aware Clustering in VANET

Location-based spatial queries (LBSQs) refer to spatial queries whose answers rely on the location of the inquirer. Efficient processing of LBSQs is of critical importance with the ever-increasing deployment and use of mobile technologies. In a mobile environment, especially when the location of the objects change frequently and many queries are being generated, a centralized system cannot handle the query well. Hence in this work we propose a hybrid mechanism of handling the query by peer node called clusters. Nodes first listen to a broadcasting server which gets the information by beacon packets sent by the wireless nodes. In this paper, we focus on the propagation of local information originating in vehicles that is useful for other vehicles in the system. This type of activity has been described as Information Warning Functions (IWF).

A Hierarchical Grid Index (HGI), spatial queries in wireless data broadcasting

The main requirements for spatial query processing via mobile terminals include rapid and accurate searching and low energy consumption. Most location-based services (LBSs) are provided using an on-demand method, which is suitable for light-loaded systems where contention for wireless channels and server processing is not severe. However, as the number of users of LBSs increases, performance deteriorates rapidly since the servers' capability to process queries is limited. Furthermore, the response time of a query may significantly increase with the concentration of users' queries in a server at the same time. That is because the server has to check the locations of users and potential objects for the final result and then individually send answers to clients via a point-to-point channel. At this time, an inefficient structure of spatial index and searching algorithm may incur an extremely large access latency. To address this problem, we propose the Hierarchical Grid Index (HGI), which provides a light-weight sequential location-based index structure for efficient LBSs. We minimize the index size through the use of hierarchical location-based identifications. And we support efficient query processing in broadcasting environments through sequential data transfer and search based on the object locations. We also propose Top-Down Search and Reduction-Counter Search algorithms for efficient searching and query processing. HGI has a simple structure through elimination of replication pointers and is therefore suitable for broadcasting environments with one-dimensional characteristics, thus enabling rapid and accurate spatial search by reducing redundant data. Our performance evaluation shows that our proposed index and algorithms are accurate and fast and support efficient spatial query processing.

Distributed Spatial k-dominant Skyline Maintenance Using Computational Object Preservation

The existing k-dominant skyline solutions are restricted to centralized query processors, limiting scalability, and imposing a single point of failure. To overcome those problems in this paper, we propose the computation and maintenance algorithms for spatial k-dominant skyline query processing in large-scale distributed environment. Where the underlying dataset is partitioned into geographically distant computing core (personal computer) that are connected to the coordinator (server). Our proposed techniques preserve the spatial k-dominant computation object itself into a serialized form. This preservation is done in client’s core after completing a computational job successfully. When the issue of maintenance comes in action, preserve data object retrieves and use for computation. This procedure eliminates the necessity of intermediate re-send and re-computation of k-dominant skyline for the maintenance issue. Thus, we quantify the gain of data transferring consecutively into different cores to maximize the overall gain as well as the query or balancing the load on different cores fairly. Extensive performance study shows that proposed algorithms are efficient and robust to different data distributions. Â

Query Processing over Uncertain Databases

Due to measurement errors, transmission lost, or injected noise for privacy protection, uncertainty exists in the data of many real applications. However, query processing techniques for deterministic data cannot be directly applied to uncertain data because they do not have mechanisms to handle data uncertainty. Therefore, efficient and effective manipulation of uncertain data is a practical yet challenging research topic. In this book, we start from the data models for imprecise and uncertain data, move on to defining different semantics for queries on uncertain data, and finally discuss the advanced query processing techniques for various probabilistic queries in uncertain databases. The book serves as a comprehensive guideline for query processing over uncertain databases. Table of Contents: Introduction / Uncertain Data Models / Spatial Query Semantics over Uncertain Data Models / Spatial Query Processing over Uncertain Databases / Conclusion

Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data.

Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the "big data" challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce.

Joint Top-K Spatial Keyword Query Processing

Web users and content are increasingly being geopositioned, and increased focus is being given to serving local content in response to web queries. This development calls for spatial keyword queries that take into account both the locations and textual descriptions of content. We study the efficient, joint processing of multiple top-k spatial keyword queries. Such joint processing is attractive during high query loads and also occurs when multiple queries are used to obfuscate a user's true query. We propose a novel algorithm and index structure for the joint processing of top-k spatial keyword queries. Empirical studies show that the proposed solution is efficient on real data sets. We also offer analytical studies on synthetic data sets to demonstrate the efficiency of the proposed solution.