K-Nearest Neighbor Search for Moving Query Point

Song, Zhexuan; Roussopoulos, Nick

doi:10.1007/3-540-47724-1_5

Cited by 278 publications

(215 citation statements)

References 12 publications

(15 reference statements)

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“…Instead, SOLE updates the query result by computing and sending only updates of the previously reported answer. This is in contrast to previous continuous query approaches (e.g., [21,34,46,50,51,60,61]) that abstract the continuous queries to a set of snapshot queries that are continuously reevaluated with the change of data inputs or queries. SOLE distinguishes between two types of query updates: Positive updates and negative updates.…”

Section: Input/output Modelmentioning

confidence: 96%

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

Mokbel

Aref

2007

The VLDB Journal

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This paper presents the Scalable On-Line Execution algorithm (SOLE, for short) for continuous and on-line evaluation of concurrent continuous spatiotemporal queries over data streams. Incoming spatiotemporal data streams are processed in-memory against a set of outstanding continuous queries. The SOLE algorithm utilizes the scarce memory resource efficiently by keeping track of only the significant objects. In-memory stored objects are expired (i.e., dropped) from memory once they become insignificant. SOLE is a scalable algorithm where all the continuous outstanding queries share the same buffer pool. In addition, SOLE is presented as a spatio-temporal join between two input streams, a stream of spatio-temporal objects and a stream of spatio-temporal queries. To cope with intervals of high arrival rates of objects and/or queries, SOLE utilizes a load-shedding approach where some of the stored objects are dropped from memory. SOLE is implemented as a pipelined query operator that can be combined with traditional query operators in a query execution plan to support a wide variety of continuous queries. Performance experiments based on a real implementation of SOLE inside a prototype of a data stream management system show the scalability and efficiency of SOLE in highly dynamic environments.

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Section: Input/output Modelmentioning

confidence: 96%

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

Mokbel

Aref

2007

The VLDB Journal

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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%

“…An important class of location based queries consists of proximity queries such as k Nearest Neighbor(kNN) query [15,32,21,6,7] and its variations, e.g., Reverse k Nearest Neighbor (RkNN) [23,29], k Aggregate Nearest Neighbor (kANN) [28]. The proximity queries in general search for data objects that minimize a distance-based function with reference to one or more query objects.…”

Section: Introductionmentioning

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%

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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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“…Tao et al [11] propose query processing methods that use R-Tree as the underlying data structure to address nearest neighbors for a query point that is moving on a straight line segment. Song and Roussopoulos [10] propose utilizing the information contained in the result sets of the previous sampled positions to answer the new queries for KNN. By caching the results of the previous queries, their algorithm provides a more ef®cient approach when the query points are moving.…”

Section: Related Workmentioning

confidence: 99%

A road network embedding technique for k-nearest neighbor search in moving object databases

Shahabi

Kolahdouzan

Sharifzadeh

2002

Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems

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A very important class of queries in GIS applications is the class of K-nearest neighbor queries. Most of the current studies on the K-nearest neighbor queries utilize spatial index structures and hence are based on the Euclidean distances between the points. In real-world road networks, however, the shortest distance between two points depends on the actual path connecting the points and cannot be computed accurately using one of the Minkowski metrics. Thus, the Euclidean distance may not properly approximate the real distance. In this paper, we apply an embedding technique to transform a road network to a high dimensional space in order to utilize computationally simple Minkowski metrics for distance measurement. Subsequently, we extend our approach to dynamically transform new points into the embedding space. Finally, we propose an ef®cient technique that can ®nd the actual shortest path between two points in the original road network using only the embedding space. Our empirical experiments indicate that the Chessboard distance metric L ? in the embedding space preserves the ordering of the distances between a point and its neighbors more precisely as compared to the Euclidean distance in the original road network.

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K-Nearest Neighbor Search for Moving Query Point

Cited by 278 publications

References 12 publications

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

Indexing Network Voronoi Diagrams

A road network embedding technique for k-nearest neighbor search in moving object databases

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