K-nearest Neighbor Queries Research Articles

Scalable 3D Spatial Queries for Analytical Pathology Imaging with MapReduce.

3D analytical pathology imaging examines high resolution 3D image volumes of human tissues to facilitate biomedical research and provide potential effective diagnostic assistance. Such approach - quantitative analysis of large-scale 3D pathology image volumes - generates tremendous amounts of spatially derived 3D micro-anatomic objects, such as 3D blood vessels and nuclei. Spatial exploration of such massive 3D spatial data requires effective and efficient querying methods. In this paper, we present a scalable and efficient 3D spatial query system for querying massive 3D spatial data based on MapReduce. The system provides an on-demand spatial querying engine which can be executed with as many instances as needed on MapReduce at runtime. Our system supports multiple types of spatial queries on MapReduce through 3D spatial data partitioning, customizable 3D spatial query engine, and implicit parallel spatial query execution. We utilize multi-level spatial indexing to achieve efficient query processing, including global partition indexing for data retrieval and on-demand local spatial indexing for spatial query processing. We evaluate our system with two representative queries: 3D spatial joins and 3D k-nearest neighbor query. Our experiments demonstrate that our system scales to large number of computing nodes, and efficiently handles data-intensive 3D spatial queries that are challenging in analytical pathology imaging.

Real-Time Spatial Queries for Moving Objects Using Storm Topology

With the rapid development of mobile data acquisition technology, the volume of available spatial data is growing at an increasingly fast pace. The real-time processing of big spatial data has become a research frontier in the field of Geographic Information Systems (GIS). To cope with these highly dynamic data, we aim to reduce the time complexity of data updating by modifying the traditional spatial index. However, existing algorithms and data structures are based on single work nodes, which are incapable of handling the required high numbers and update rates of moving objects. In this paper, we present a distributed spatial index based on Apache Storm, an open-source distributed real-time computation system. Using this approach, we compare the range and K-nearest neighbor (KNN) query efficiency of four spatial indexes on a single dataset and introduce a method of performing spatial joins between two moving datasets. In particular, we build a secondary distributed index for spatial join queries based on the grid-partition index. Finally, a series of experiments are presented to explore the factors that affect the performance of the distributed index and to demonstrate the feasibility of the proposed distributed index based on Storm. As a real-world application, this approach has been integrated into an information system that provides real-time traffic decision support.

A reusable and single-interactive model for secure approximate k-nearest neighbor query in cloud

In sensitive database applications (e.g., time series, scientific databases, and biometric), where database is encrypted and outsourced to a public cloud, secure approximate k-nearest neighbor (SANN) query is a fundamental research topic, aiming at retrieving high-dimensional objects that are similar to a given query from encrypted database. To process such queries without ever decrypting the data in cloud is still a challenging task. Existing works encounter various inherent limitations, such as query distinguishability, low-level efficiency and non-recoverability. All of them lead to either fragile security or low accuracy. Hence, the majority of existing works in this field are impractical for industrial applications.In this work, we present a novel model to remove the above limitations. Specifically, a reusable and single-interactive SANN paradigm is proposed in Euclidean high-dimensional space. Firstly, we present a secure variation of B+-tree (i.e., Bc-tree) to quickly locate high-dimensional candidates in cloud by leveraging on comparable encryption. Based on that, an arbitrary query requestor acquires approximate k-nearest neighbors by linearly scanning over candidates. Meanwhile, two refinements, multi-index strategy and boosting refinement strategy, are proposed to further improve the accuracy of search result and overcome the high-dependency of bandwidth, respectively. In the end, through extensive evaluations on four data sets, the proposed mechanisms are demonstrated to be superior in the tradeoff between accuracy and security.

A Novel Dummy-Based KNN Query Anonymization Method in Mobile Services

Due to the advances of mobile devices with GPS (Global Positioning System), a user's privacy threat is increased in location based services (LBSs).So, various Location Privacy-Preserving Mechanisms (LPPMs) have been proposed in the literature to address the privacy risks derived from the exposure of user locations through the use of LBSs. However, these methods obfuscate the locations disclosed to the LBS provider using a variety of strategies, most of which come at a cost of resource consumption. Therefore, we propose a privacy-protected KNN query anonymization method based on Bayesian estimation for Location-based services. Unlike previous dummy-based approaches, in our method, the request to the LBS server doesn't contain the genuine user location, so we can't calculate whether meet the threshold condition of two location directly, but must to decision making by transition probability. In addition, our method just requires the server returns the results the client needs. Further, we propose an effective search algorithm to improve the server processing. So it can reduce bandwidth usages and efficiently support K-nearest neighbor queries without revealing the private information of the query issuer. An empirical study shows that our proposal is effective in terms of offering location privacy, and efficient in terms of computation and communication costs. Keyword: Location privacy protection, Location-based services(LBSs),K-nearest neighbor query, Bayesian estimation

Keyword K-Nearest Neighbor Query of Road Network under Wireless Broadcast Environment

Keyword K-Nearest Neighbor Query of Road Network under Wireless Broadcast Environment

K-Multi-preference query over road networks

Nowadays, the road network has gained more and more attention in the research area of databases. Existing works mainly focus on standalone queries, such as k-nearest neighbor queries over a single type of objects (e.g., facility like restaurant or hotel). In this paper, we propose a k-multi-preference (kMP) query over road networks, involving complex query predicates and multiple facilities. In particular, given a query graph, a kMP query retrieves of the top-k groups of vertices (of k facility types) satisfying the label constraints and their aggregate distances are the smallest. A naive solution to this problem is to enumerate all combinations of vertices with k possible facility types and then select the one with the minimum sum distance. This method, however, incurs rather high computation cost due to exponential possible combinations. In addition, the existing solutions to other standalone queries are for a single type of facilities and cannot be directly used to answer kMP queries. Therefore, in this paper, we propose an efficient approach to process a kMP query, which utilizes an index with bounded space and reduces the computation cost of the shortest path queries. We also design effective pruning techniques to filter out false alarms. Through our extensive experiments, we demonstrate the efficiency and effectiveness of our proposed solutions.

Enabling high-dimensional range queries using kNN indexing techniques: approaches and empirical results

Many modern search applications are high-dimensional and depend on efficient orthogonal range queries. These applications span web-based and scientific needs as well as uses for data mining. Although k-nearest neighbor queries are becoming increasingly common due to mobile and geospatial applications, orthogonal range queries in high-dimensional data remain extremely important and relevant. For efficient querying, data is typically stored in an index optimized for either kNN or range queries. This can be problematic when data is optimized for kNN retrieval and a user needs a range query or vice versa. Here, we address the issue of using a kNN-based index for range queries, as well as outline the general computational geometry problem of adapting these systems to range queries. We refer to these methods as space-based decompositions and provide a straightforward heuristic for this problem. Using iDistance as our applied kNN indexing technique, we also develop an optimal (data-based) algorithm designed specifically for its indexing scheme. We compare this method to the suggested naive approach using real world datasets. The data-based algorithm consistently performs better.

Moving range k nearest neighbor queries with quality guarantee over uncertain moving objects

To avoid traffic accidents, drivers must constantly be aware of nearby vehicles. Unfortunately, nearby vehicles often go unnoticed because of various obstacles such as other vehicles, buildings, or poor weather. In this paper, we study Moving range k-nearest neighbor (MRkNN) queries as a tool for continuously monitoring nearby moving objects. A simple approach to processing MRkNN queries is to have each object periodically broadcast information regarding its movements (i.e., location and velocity at a particular time) to other objects. However, this simple technique cannot be used to process MRkNN queries owing to the limited network bandwidth in mobile peer-to-peer environments. Therefore, we address this bandwidth limitation by proposing a probabilistic algorithm, called MINT, for MovIng range k-NN queries with qualiTy guarantee over uncertain moving objects. MINT provides users with approximate answers with a quality guarantee, rather than exact answers, with near optimal communication costs. Using a series of simulations, we demonstrate the efficiency and efficacy of MINT in evaluating MRkNN queries with a quality guarantee.

A Bitmap based Data Encryption Scheme in Cloud Computing

With the development of cloud computing, the interest on spatial database outsourcing has been sharply increasing. Therefore, researches on data encryption methods for protecting location data privacy in outsourced databases have been actively performed. However, the existing data encryption schemes do not consider data distribution when generating data clusters and they use a tree-based index for processing k-nearest neighbor queries. As a result, the performance of query processing algorithm can fluctuate depending on the tree depth. To solve these problems, we propose a densityaware data encryption scheme and a query processing algorithm for database outsourcing. Our density-aware data encryption scheme uses a grid index to generate clusters and transforms the original data information into a bitmap. To provide efficient query processing, we use an algebraic coding based hash index to reduce the data retrieval time. Finally, in performance analysis, we show that the proposed scheme provides better query processing performance and guarantees the privacy of users, compared with the existing schemes.

A Bitmap based Data Encryption Scheme in Cloud Computing

With the development of cloud computing, the interest on spatial database outsourcing has been sharply increasing. Therefore, researches on data encryption methods for protecting location data privacy in outsourced databases have been actively performed. However, the existing data encryption schemes do not consider data distribution when generating data clusters and they use a tree-based index for processing k-nearest neighbor queries. As a result, the performance of query processing algorithm can fluctuate depending on the tree depth. To solve these problems, we propose a densityaware data encryption scheme and a query processing algorithm for database outsourcing. Our density-aware data encryption scheme uses a grid index to generate clusters and transforms the original data information into a bitmap. To provide efficient query processing, we use an algebraic coding based hash index to reduce the data retrieval time. Finally, in performance analysis, we show that the proposed scheme provides better query processing performance and guarantees the privacy of users, compared with the existing schemes.

Scalable Distributed Processing of K Nearest Neighbor Queries over Moving Objects

Central to many applications involving moving objects is the task of processing k-nearest neighbor (k-NN) queries. Most of the existing approaches to this problem are designed for the centralized setting where query processing takes place on a single server; it is difficult, if not impossible, for them to scale to a distributed setting to handle the vast volume of data and concurrent queries that are increasingly common in those applications. To address this problem, we propose a suite of solutions that can support scalable distributed processing of k-NN queries. We first present a new index structure called Dynamic Strip Index (DSI), which can better adapt to different data distributions than exiting grid indexes. Moreover, it can be naturally distributed across the cluster, therefore lending itself well to distributed processing. We further propose a distributed k-NN search (DKNN) algorithm based on DSI. DKNN avoids having an uncertain number of potentially expensive iterations, and is thus more efficient and more predictable than existing approaches. DSI and DKNN are implemented on Apache S4, an open-source platform for distributed stream processing. We perform extensive experiments to study the characteristics of DSI and DKNN, and compare them with three baseline methods. Experimental results show that our proposal scales well and significantly outperforms the alternative methods.

Triggered query with strong location privacy in mobile network

Location privacy is a hot-button topic that has to be taken into account if location-based services (LBS) are to succeed. Extensive researches focus on the nearest neighbor (NN) query or k-nearest neighbor (kNN) query about location privacy-preserving. However, no single technique can be applied to any situation and achieve high security and low cost. This manuscript focuses on the location privacy-preserving in the geo-fencing services, A secure two-party computation location privacy model and the corresponding solution was proposes based on triggered query. The author draw on the computational geometry and cryptography technologies, mainly to conquer such problems related to the users' location hidden, secret checking-in and secret authentication in the geo-fencing services. Performance assessment shows that the proposed solution can reduce the query-processing time and the size of query result set.

An Efficient Scalable Spatial Data Search for Location-Aware Mobile Services

Various studies related to spatial indices are currently being conducted. Most of these studies focus on lowering the costs for indexed searches in on-demand environments during spatial query processing. However, on-demand environments often have issues with service delays when there is a congestion of the query on the server when dealing with information about complicated moving objects. Therefore, a study on spatial indices that considers broadcast environment is required. Studies on information transfer in broadcast environments should consider the characteristics of one-dimensional sequential flow. However, studies thus far on indexing methods that consider such characteristic are insufficient. In particular, there has been no study that considers both the data transfer sequence and an index structure capable of managing a moving object within a broadcast environment. In this paper, we propose a data transfer method that considers the location/distribution of objects and the air index structure for the location management of moving objects within a broadcast environment. The results from theoretical analyses and experiments show the proposed algorithms that use the GSI method are scalable and energy efficient for range queries and k-nearest neighbor queries.

A Spatial Indexing Scheme for Location Based Service Queries in a Single Wireless Broadcast Channel

The rapid advancement of mobile devices and wireless network technology has accelerated the development of applications in the mobile computing field. In particular, LBS (Location-Based Services) are one of the fastest growing areas in mobile applications. In a wireless data broadcasting environment, a server can effectively provide LBS to a large population of mobile clients. The two most typical types of LBS queries are window and kNN (k-Nearest Neighbors) queries. Previously proposed window and kNN query processing schemes in a single wireless broadcast channel environment tend to access much unnecessary index information and data objects due to their low filtering powers. In this paper, we propose a new spatial index called HMI (Hilbert curve-based MBR filtering Index) for the efficient processing of window and kNN queries in a single wireless broadcast channel. HMI is a tree-structured index whose construction is based on the Hilbert curve and MBR (Minimum Bounding Rectangle). By combining an MBR structure with the Hilbert curve order, HMI obtains the advantages of both structures. We present an in-depth experimental analysis of our method by comparing it with current existing schemes. Our performance analysis shows that HMI significantly decreases the average access and tuning times of kNN queries over current existing schemes. HMI also gives better average tuning times and comparable average access times of window queries over current existing schemes.

빅데이터 환경에서 연속 질의 처리를 위한 리버스 k-최근접 질의 처리 기법

빅데이터 환경에서 연속 질의 처리를 위한 리버스 k-최근접 질의 처리 기법

Processing KNN Queries in Grid-Based Sensor Networks

Recently, developing efficient processing techniques in spatio-temporal databases has been a much discussed topic. Many applications, such as mobile information systems, traffic control system, and geographical information systems, can benefit from efficient processing of spatio-temporal queries. In this paper, we focus on processing an important type of spatio-temporal queries, the K-nearest neighbor (KNN) queries. Different from the previous research, the locations of objects are located by the sensors which are deployed in a grid-based manner. As the positioning technique used is not the GPS technique, but the sensor network technique, this results in a greater uncertainty regarding object location. With the uncertain location information of objects, we try to develop an efficient algorithm to process the KNN queries. Moreover, we design a probability model to quantify the possibility of each object being the query result. Finally, extensive experiments are conducted to demonstrate the efficiency of the proposed algorithms.

On reverse-k-nearest-neighbor joins

A reverse k-nearest neighbour (RkNN) query determines the objects from a database that have the query as one of their k-nearest neighbors. Processing such a query has received plenty of attention i...

A privacy-aware monitoring algorithm for moving $$k$$ k -nearest neighbor queries in road networks

Location privacy is a major obstacle in the ubiquitous deployment of mobile and pervasive computing services. In this study, we present a new approach for preserving the trajectory privacy of moving $$k$$k-nearest neighbor (M$$k$$kNN) queries in road networks. Several location anonymization algorithms have been proposed for providing location privacy to users traveling on a road network. These algorithms focus primarily on the location anonymization of snapshot queries. Indeed, users move freely and arbitrarily, and thus query results provided to them soon become invalid as their locations change. To refresh the query result, each user must therefore periodically contact the location-based service, enabling attackers to identify and track the user easily. In addition, frequent location updates for the user may incur severe computational and communication costs. We address these issues by proposing a privacy-aware monitoring algorithm, called PAMA, for preserving the trajectory privacy of M$$k$$kNN queries in road networks. Our simulation results show that PAMA significantly outperforms conventional algorithms in terms of both security and performance.

Novel itinerary-based KNN query algorithm leveraging grid division routing in wireless sensor networks of skewness distribution

Wireless sensor networks (WSN) is a key enabling technique for achieving the vision of the Internet of Things. In many applications of WSN such as environmental monitoring and vehicle tracking, they may require to launch spatial queries for collecting and gathering sensory data for achieving certain goals. One such query is the $$K$$ K nearest neighbor (KNN) query, which aims to collect sensory data from $$k$$ k sensor nodes nearest to a certain query location. Techniques, namely the itinerary-based KNN query algorithms, are recently developed for facilitating KNN queries. Generally, these techniques propagate queries and collect data along a predetermined itinerary. However, query accuracy and boundary expansion are two challenges that are not well addressed. To mitigate these issues, in this paper, we propose a novel KNN query algorithm based on grid division routing in the setting of skewness distribution, where the itinerary is formed based on the connectivity of adjacent grid cells centers. This technique can achieve better query accuracy and cause less energy consumption by executing the query concurrently in subregions. Besides, the void region problem is well addressed based on the proximity of neighbor grid cells. Experiment result shows that our technique performs better in several aspects including query accuracy, data redundancy, and energy efficiency.

A collaborative approach to moving [formula omitted]-nearest neighbor queries in directed and dynamic road networks

In this paper, we investigate a new approach to moving k-nearest neighbor (MkNN) queries in directed and dynamic road networks, where each road segment has a particular orientation and its travel time changes depending on traffic conditions. An MkNN query continuously finds the k nearest neighbors (NNs) of a moving query object. Most existing studies have focused on MkNN queries in undirected and static road networks, where each road segment is bidirectional and its travel time does not change over time. However, little attention has been paid to MkNN queries in directed and dynamic road networks. In this research, we propose COMET, a collaborative approach to Moving k nEaresT neighbor queries in directed and dynamic road networks, where query processing is performed through collaboration between the server and query objects. In addition, we conduct extensive experiments to show that COMET substantially outperforms a conventional method in terms of query response time, bandwidth usage, and energy consumption.