Location-based spatial queries

Zhang, Jun; Zhu, Ming; Papadias, Dimitris; Tao, Yufei; Lee, Dik Lun

doi:10.1145/872757.872812

Cited by 245 publications

(127 citation statements)

References 19 publications

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“…This technique has been extended to handle different related problems, including database services in wireless broadcast environments [2], [3]; high-dimensional query evaluation [4]; continuous location-based services [5]- [7]; and virus spread analysis among mobile devices [8]. Conceptually, the Voronoi diagram partitions the data space into disjoint "Voronoi cells", so that all points in the same Voronoi cell have the same nearest neighbor.…”

Section: Introductionmentioning

confidence: 99%

UV-diagram: A Voronoi diagram for uncertain data

Cheng

Xie

Yiu

et al. 2010

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)

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Abstract-The Voronoi diagram is an important techniquefor answering nearest-neighbor queries for spatial databases. In this paper, we study how the Voronoi diagram can be used on uncertain data, which are inherent in scientific and business applications. In particular, we propose the Uncertain-Voronoi Diagram (or UV-diagram in short). Conceptually, the data space is divided into distinct "UV-partitions", where each UV-partition P is associated with a set S of objects; any point q located in P has the set S as its nearest neighbor with non-zero probabilities. The UV-diagram facilitates queries that inquire objects for having non-zero chances of being the nearest neighbor of a given query point. It also allows analysis of nearest neighbor information, e.g., finding out how many objects are the nearest neighbors in a given area.However, a UV-diagram requires exponential construction and storage costs. To tackle these problems, we devise an alternative representation for UV-partitions, and develop an adaptive index for the UV-diagram. This index can be constructed in polynomial time. We examine how it can be extended to support other related queries. We also perform extensive experiments to validate the effectiveness of our approach.

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Section: Introductionmentioning

confidence: 99%

UV-diagram: A Voronoi diagram for uncertain data

Cheng

Xie

Yiu

et al. 2010

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)

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“…The research on kNN query processing can be categorized into two main areas, namely, Euclidean space and road networks. In the past, numerous algorithms (e.g., [32,30,21,24,9]) have been proposed to solve kNN problem in the Euclidean space. All of these approaches are applicable to the spaces where the distance between objects is only a function of their spatial attributes (e.g., Euclidean distance).…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the main research focus has been on indexing the objects to avoid the exhaustive search. Earlier studies assumed Euclidean distance as the distance function and hence indexed the objects in Euclidean space (e.g., [32,30,21,24]) using R-tree [4] like index structures. With the advent of online mapping systems such as Google Maps and Mapquest and the availability of accurate nation-wide road network data, the proximity queries have been extended from Euclidean space to the road network space as natural artifact.…”

Section: Introductionmentioning

confidence: 99%

Indexing Network Voronoi Diagrams

Demiryurek

Shahabi

2012

Database Systems for Advanced Applications

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Abstract. The Network Voronoi diagram and its variants have been extensively used in the context of numerous applications in road networks, particularly to efficiently evaluate various spatial proximity queries such as k nearest neighbor (kNN), reverse kNN, and closest pair. Although the existing approaches successfully utilize the network Voronoi diagram as a way to partition the space for their specific problems, there is little emphasis on how to efficiently find and access the network Voronoi cell containing a particular point or edge of the network. In this paper, we study the index structures on network Voronoi diagrams that enable exact and fast response to contain query in road networks. We show that existing index structures, treating a network Voronoi cell as a simple polygon, may yield inaccurate results due to the network topology, and fail to scale to large networks with numerous Voronoi generators. With our method, termed Voronoi-Quad-tree (or VQ-tree for short), we use Quad-tree to index network Voronoi diagrams to address both of these shortcomings. We demonstrate the efficiency of VQ-tree via experimental evaluations with real-world datasets consisting of a variety of large road networks with numerous data objects.

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“…However, the focus has been on the efficient computation and representation of these diagrams for an entire set of points. The only research works on computing Voronoi cell of a point is the two approaches presented in Stanoi et al [13] and Zhang et al [15]. Stanoi et al [13] study the problem of finding the influence set (Voronoi cell) of a query point considering data points stored in the database.…”

Section: Related Workmentioning

confidence: 99%

“…However, we use a radial range to be compatible with the communication style of the nodes. Zhang et al [15] also propose an approach to compute the cell (validity region in their terminology) when the data points are indexed by an R-tree. Their ray shooting scheme benefits from time parameterized nearest neighbor queries using an R-tree.…”

Section: Related Workmentioning

confidence: 99%

Utilizing Voronoi Cells of Location Data Streams for Accurate Computation of Aggregate Functions in Sensor Networks

Sharifzadeh

Shahabi

2007

Geoinformatica

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Sensor networks are unattended deeply distributed systems whose database schema can be conceptualized using the relational model. Aggregation queries on the data sampled at each sensor node are the main means to extract the abstract characteristics of the surrounding environment. However, the non-uniform distribution of the sensor nodes in the environment leads to inaccurate results generated by the aggregation queries. In this paper, we introduce Bspatial aggregations^that take into consideration the spatial location of each measurement generated by the sensor nodes. We propose the use of spatial interpolation methods derived from the fields of spatial statistics and computational geometry to answer spatial aggregations. In particular, we study Spatial Moving Average (SMA), Voronoi Diagram and Triangulated Irregular Network (TIN). Investigating these methods for answering spatial average queries, we show that the average value on the data samples weighted by the area of the Voronoi cell of the corresponding sensor node, provides the best precision. Consequently, we introduce an algorithms to compute and maintain the accurate Voronoi cell at each sensor node while the location of the others arrive on data stream. We also propose AVC-SW, a novel algorithm to approximate this Voronoi cell over a sliding window that supports dynamism in the sensor network. To demonstrate the performance of in-network implementation of our aggregation operators, we have developed prototypes of two different approaches to distributed spatial aggregate processing.

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Location-based spatial queries

Cited by 245 publications

References 19 publications

UV-diagram: A Voronoi diagram for uncertain data

UV-diagram: A Voronoi diagram for uncertain data

Indexing Network Voronoi Diagrams

Utilizing Voronoi Cells of Location Data Streams for Accurate Computation of Aggregate Functions in Sensor Networks

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