An Efficient Technique for Continuous K-Nearest Neighbor Query Processing on Moving Objects in a Road Network

One of the most important queries in spatio-temporal databases that aim at managing moving objects efficiently is the continuous K-nearest neighbor (CKNN) query. A CKNN query is to retrieve the K-nearest neighbors (KNNs) of a moving user at each time instant within a user-given time interval [t s , t e ]. In this paper, we investigate how to process a CKNN query efficiently. Different from the previous related works, our work relieves the past assumption, that an object moves with a fixed velocity, by allowing that the velocity of the object can vary within a known range. Due to the introduction of this uncertainty on the velocity of each object, processing a CKNN query becomes much more complicated. We will discuss the complications incurred by this uncertainty and propose a cost-effective P2 KNN algorithm to find the objects that could be the KNNs at each time instant within the given query time interval. Besides, a probability-based model is designed to quantify the possibility of each object being one of the KNNs. Comprehensive experiments demonstrate the efficiency and the effectiveness of the proposed approach.

In this paper we propose a strategy for continuous k-nearest neighbour query processing for location-based services. Our approach applies clustering, which has not been applied to k-nearest neighbour processing by other works. Using a clustered point set on the server, a safe region is formed using a subset of the existing clusters. As long as the user's location (i.e., query point) remains in the safe region, the data set on the server can be used to accurately answer all k-nearest neighbour queries the majority of the time. Our strategy is an approximation strategy, as there are situations where the result produced for the user may not be accurate. However, an evaluation of our strategy show that the result is accurate at least 70% of the time in most cases. We also observed that when compared to repeated k-nearest neighbour search, our strategy is computationally significantly faster for a larger dataset. Therefore, it is worth the trade-off of less than 100% accuracy to achieve results quickly, and for several applications (e.g., restaurant searching), this can be ideal.

In the real-time series streaming environments, such as data analysis in sensor networks, online stock analysis, video surveillance and weather forecasting, similarity search, which aims at retrieving the similarity between two or more streams, is a hot issue in the recent years. How to find continuous k-nearest neighbors (CKNN) queries has been one of the most common applications in computing on DSMS. In this paper, we developed traditional skylines technique and propose W-Skyline to process CKNN queries as a bandwidth efficient approach over distributed streams. It tries to use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity search over time-series data in memory. Finally, we will give an extensive experimental study with real-time data sets that verifies the effectiveness of our W-Skyline transformation approach in similarity search and CKNN discovery within arbitrary ranges in the time series streaming environments.

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

A k-Range Nearest Neighbor (or kRNN for short) query in road networks finds the k nearest neighbors of every point on the road segments within a given query region based on the network distance. The kRNN query is significantly important for location-based applications in many realistic scenarios. For example, (1) the user's location is uncertain, i.e., user's location is modeled by a spatial region, and (2) the user is not willing to reveal her exact location to preserve her privacy, i.e., her location is blurred into a spatial region. However, the existing solutions for kRNN queries simply apply the traditional k-nearest neighbor query processing algorithm multiple times, which poses a huge redundant searching overhead. To this end, we propose an efficient kRNN query processing algorithm in this paper. Our algorithm (1) employs a shared execution approach to eliminate the redundant searching overhead, and (2) provides a parameter that can be tuned to achieve a tradeoff between the query processing performance and the storage overhead, while guaranteeing the user's exact k-nearest neighbors are included in the query answers. The experimental results show that our algorithm always outperforms the existing solution in terms of query response time, and the introduced tuning parameter is an effective way to achieve the tradeoff between the query response time and the storage overhead.

The range query of moving objects in a road network is widely used in battlefield environment, intelligent transportation systems, and mobile phone location. It has gradually become a research hotspot in the field of spatiotemporal data management. Most of the existing research on range query of moving objects in the road network is based on the premise of dense sampling of the device and complete storage of data. It ignores the uncertainty of discrete location data caused by a blind area of actual devices and limited memory, which cannot guarantee the accuracy of the range query. In this paper, the processing framework of probabilistic range query of moving objects in the road network is constructed. A spatiotemporal index structure is proposed based on the framework. This index can effectively represent time weights and the relationship between road sections. The probabilistic range query algorithm of moving objects in the road network with an uncertain trajectory caused by sampling frequency is designed and implemented. The experiments verify that the method proposed in this paper can improve query efficiency and ensure query accuracy.

Digital ecosystems are formed by “digital organisms” in complex, dynamic, and interrelated ecosystems and utilize multiple technologies to provide cost-efficient digital services and value-creating activities. A distributed wireless mobile network that serves as the underlying infrastructure to digital ecosystems provides important applications to the digital ecosystems, two of which are mobile navigation and continuous mobile information services. Most information and query services in a mobile environment are continuous mobile query processing or continuous k nearest neighbor (CKNN), which finds the locations where interest points or interest objects change while mobile users are moving. These locations are known as “split nodes.” All of the existing works on CKNN divide the query path into segments, which is a segment of road separated by two intersections, and then, the process to find split nodes is applied to each segment. Since there are many segments (due to many intersections, obviously), processing each segment is naturally inefficient. In this paper, we propose an alternative solution to overcome this problem. We use the Voronoi diagram for CKNN [called Voronoi CKNN (VCKNN)]. Our proposed approach does not need to divide the query path into segments, hence improving the overall query processing performance. Our experiment verified the applicability of the VCKNN approach to solve CKNN queries.

In telematics and location-based service (LBS) applications, because moving objects usually move on spatial networks, their locations are updated frequently, leading to the degradation of retrieval performance. To manage the frequent updates of moving objects’ locations in an efficient way, we propose a new distributed grid scheme which utilizes node-based pre-computation technique to minimize the update cost of the moving objects’ locations. Because our grid scheme manages spatial network data separately from the Point of Interests (POIs) and moving objects, it can minimize the update cost of the POIs and moving objects. Using our grid scheme, we propose a new k-nearest neighbor (k-NN) query processing algorithm which minimizes the number of accesses to adjacent cells during POIs retrieval in a parallel way. Finally, we show from our performance analysis that our k-NN query processing algorithm is better on retrieval performance than that of the existing S-GRID.

The continuous K nearest neighbor (CKNN) query is an important type of query that finds continuously the KNN to a query point on a given path. We focus on moving queries issued on stationary objects in Spatial Network Database (SNDB) The result of this type of query is a set of intervals (defined by split points) and their corresponding KNNs. This means that the KNN of an object traveling on one interval of the path remains the same all through that interval, until it reaches a split point where its KNNs change. Existing methods for CKNN are based on Euclidean distances. In this paper we propose a new algorithm for answering CKNN in SNDB where the important measure for the shortest path is network distances rather than Euclidean distances. We propose DAR and eDAR algorithms to address CKNN queries based on the progressive incremental network expansion (PINE) technique. Our experiments show that the eDAR approach has better response time, and requires fewer shortest distance computations and KNN queries than approaches that are based on VN3 using IE.

The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes. Because of its operation, the application of this classification may be limited to problems with a certain number of instances, particularly, when run time is a consideration. However, the classification of large amounts of data has become a fundamental task in many real-world applications. It is logical to scale the k-Nearest Neighbor method to large scale datasets. This paper proposes a new k-Nearest Neighbor classification method (KNN-CCL) which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts. The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters. The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets. Finally, sets of experiments are conducted on the UCI datasets. The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance.

In this paper, two strategies for processing a continuous k-nearest neighbor query for location-based services are proposed. Both use a spatial access method, the mqrtree, for locating a safe region. The mqrtree supports searching within the structure, so searches from the root are not required - a property which is exploited in the strategies. However, the proposed strategies will work with most spatial access methods. The strategies are evaluated and compared against a repeated nearest neighbor search. It is shown that both approaches achieve significant performance gains in reducing the number of times a new safe region must be identified, in both random and exponentially distributed points sets.

Recently, advanced multimedia applications, such as geographic information system, and content-based image/video retrieval system, require the efficient processing of k-nearest neighbor queries with semantic predicates as well as visual predicates. In this paper, we propose an integrated index structure, so-called SPY-TEC+, that provides an efficient method for indexing visual and semantic feature information at the same time using the SPY-TEC and the signature file. We also propose an efficient incremental nearest neighbor algorithm for processing the k-nearest neighbor queries with visual and semantic predicates on the SPY-TEC+.KeywordsIndex StructureQuery PointNeighbor AlgorithmVisual PredicateCover ColorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A set of distributed continual range query requests, each defining a geographical region of interest, needs to be periodically reevaluated to provide up to date answers. Processing these continual queries efficiently and incrementally becomes important for location based services and applications. In this paper, we propose an efficient incremental method for continuous range query characterized by mobility of objects which follow paths in an underlying spatial road network. We notice that both queries and moving objects can be attached to the road link for updating of query answers. By using Grid R∗-tree which indexing spatial road network data, query requests and moving objects register themselves to corresponding road links according to query areas and moving object locations on the network. Also we analyzed the impacts to query answers caused by geometry relation of road link and query area and state of moving objects. Then we proposed an efficient update algorithm and corresponding memory data structures based on spatial and state classification which formed a safe region to avoid unnecessary updates. Finally a comprehensive experimental evaluation using real data has been conducted to demonstrate the efficiency and effectiveness of our algorithm.

The k nearest neighbor (KNN) query, an essential query for information processing in sensor networks, has not received sufficient attention in the research community of sensor networks. In this paper, we examine in-network processing of KNN queries by proposing two alternative algorithms, namely the GeoRouting Tree (GRT) and the KNN Boundary Tree (KBT). The former is based on a distributed spatial index structure and prunes off the irrelevant nodes during query propagation. The latter is based upon ad-hoc geographic routing and first obtains a region within which at least k nearest sensor nodes are enclosed and then decides the k nearest nodes to the query point. We provide an extensive performance evaluation to study the impact of various system factors and protocol parameters. Our results show that GRT yields a good tradeoff between energy consumption and query accuracy in static scenarios. On the other hand, KBT achieves better energy efficiency while being more tolerant to network dynamics.

Network Voronoi Diagram on uncertain objects for nearest neighbor queries