Moving Query Object Research Articles

Processing continuous K-nearest skyline query with uncertainty in spatio-temporal databases

Continuous K-nearest skyline query (CKNSQ) is an important query in the spatio-temporal databases. Given a query time interval [ts,te] and a moving query object q, a CKNSQ is to retrieve the K-nearest skyline points of q at each time instant within [ts,te]. Different from the previous works, in this paper we devote to overcoming the specific assumptions that each object is static in road networks and has the certain dimensional values. We focus on processing the CKNSQ over moving objects with uncertain dimensional values in Euclidean space and the velocity of each object (including the query object) varies within a known range. As the uncertainty is involved, such a query is called the continuous possible K-nearest skyline query (CPKNSQ). We first discuss the difficulties raised by the uncertainty of moving objects and then propose the CPKNSQ algorithm operated with a data-partitioning index, the uncertain TPR-tree (UTPR-tree), to efficiently answer the CPKNSQ. Moreover, we design a probability-based model to quantify the possibility of each object being the query result. Finally, extensive experiments using the synthetic and real datasets demonstrate the effectiveness and the efficiency of the proposed approaches.

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.

Efficient processing of continuous min–max distance bounded query with updates in road networks

In the last decade, the research community focuses on the design of various methods in support of spatio-temporal queries in road networks. In this paper, we present a novel and important query, named the continuous min–max distance bounded query (CM2DBQ). Given a moving query object q, a minimal distance dm, and a maximal distance dM, a CM2DBQ retrieves the bounded objects whose road distances to q are within the range [dm,dM] at each time instant. We propose two algorithms, named the Continuous Within Query-based (CWQ-based) algorithm and the CM2DBQalgorithm, to efficiently determine the bounded objects. In addition, we design a mechanism, named the bounded objects updating mechanism, to rapidly evaluate the new query result when object updates occur. Extensive experiments using real road network dataset demonstrate the efficiency of the proposed algorithms and the usefulness of the bounded objects updating mechanism.

Reachable Nearest Neighbour Query Processing of R-Trees for Moving Query Object

storage and retrieval of multidimensional data in large volumes has become one of the key issues in the design and implementation of commercial and application software. The kind of queries posted on such data is also multifarious. Nearest neighbor queries are one such category and have more significance in GIS type of application. R-tree and its sequel are data partitioned hierarchical multidimensional indexing structures that help in this purpose. While general approaches are available in literature that discussing finding of Nearest neighbor for moving query point, few have explored on visible NN queries, but retrieved NN object may not always be reachable from the query object, since some obstacle objects (i.e. Hills, Rivers, Vallies) might be in between query point and NN point. This paper proposes Reachable Nearest neighbor queries for moving query object. The results are compared graphically with existing models, the proposed model out performs the existing models in a significant way.

Scalable processing of continuous K-nearest neighbor queries with uncertain velocity

Continuous K-nearest neighbor (C KNN) query is an important type of spatio-temporal queries. Given a time interval [ t s , t e ] and a moving query object q, a C KNN query is to find the K-nearest neighbors ( KNNs) of q at each time instant within [ t s , t e ]. In this paper, we focus on the issue of scalable processing of C KNN queries over moving objects with uncertain velocity. Due to the large amount of C KNN queries that need to be evaluated concurrently, efficiently processing such queries inevitably becomes more complicated. We propose an index structure, namely the CI-tree, to predetermine and organize the candidates for each query issued by the user from anywhere and anytime. When the C KNN queries are evaluated, their corresponding candidates can be rapidly retrieved by traversing the CI-tree so that the processing time is greatly reduced. A comprehensive set of experiments is performed to demonstrate the effectiveness and the efficiency of the CI-tree.

Efficient evaluation of continuous spatio-temporal queries on moving objects with uncertain velocity

Continuous Range (CR) query and Continuous K-Nearest Neighbor (CKNN) query are two important types of spatio-temporal queries. Given a time interval [t s , t e ] and a moving query object q, a CR query is to find the moving objects whose Euclidean distances to q are within a user-given distance at each time instant within [t s , t e ]. A CKNN query is to retrieve the K-Nearest Neighbors (KNNs) of this query object q at each time instant within [t s , t e ]. In this paper, we investigate how to process these spatio-temporal queries efficiently under the situation that the velocity of each object is not fixed. This uncertainty on the velocity of object inevitably results in high complexity in processing spatio-temporal queries. We will discuss the complications incurred by this uncertainty and propose two algorithms, namely the Possibility-based possible within objects searching algorithm and the Possibility-based possible KNN searching algorithm, for the CR query and the CKNN query, respectively. A Possibility-based model is designed accordingly to quantify the possibility of each object being the result of a CR query or a CKNN query. Comprehensive experiments are performed to demonstrate the effectiveness and the efficiency of the proposed approaches.