Spatial Queries in the Presence of Obstacles

Zhang, Jun; Papadias, Dimitris; Mouratidis, Kyriakos; Zhu, Ming

doi:10.1007/978-3-540-24741-8_22

Cited by 47 publications

(40 citation statements)

References 20 publications

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“…Zhang et al [31] propose several algorithms for processing common spatial queries such as range queries, NN search, edistance join queries, closest pair queries and distance semi-join queries, in the obstructed space. Xia et al [29] present a more detailed study of the obstructed nearest neighbor (ONN) query which finds the k (≥ 1) NNs of a given query point according to the obstructed distance.…”

Section: Queries With Obstaclesmentioning

confidence: 99%

“…As pointed out in Section 1, a naive approach is to perform ONN retrieval [31] at every single point of a specified query line segment q. However, it is not feasible due to the unlimited number of points along q.…”

Section: Definition 6 (Continuous Obstructed Nearest Neighbor Query)mentioning

confidence: 99%

“…A naive approach is to issue an obstructed nearest neighbor (ONN) search [31] at every point of a specified query line segment q. However, this approach is definitely infeasible as the number of points on q is infinite.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Continuous obstructed nearest neighbor queries in spatial databases

Gao

Zheng

2009

Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data

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In this paper, we study a novel form of continuous nearest neighbor queries in the presence of obstacles, namely continuous obstructed nearest neighbor (CONN) search. It considers the impact of obstacles on the distance between objects, which is ignored by most of spatial queries. Given a data set P, an obstacle set O, and a query line segment q in a two-dimensional space, a CONN query retrieves the nearest neighbor of each point on q according to the obstructed distance, i.e., the shortest path between them without crossing any obstacle. We formulate CONN search, analyze its unique properties, and develop algorithms for exact CONN query processing, assuming that both P and O are indexed by conventional data-partitioning indices (e.g., R-trees). Our methods tackle the CONN retrieval by performing a single query for the entire query segment, and only process the data points and obstacles relevant to the final result, via a novel concept of control points and an efficient quadraticbased split point computation algorithm. In addition, we extend our solution to handle the continuous obstructed k-nearest neighbor (COkNN) search, which finds the k (≥ 1) nearest neighbors to every point along q based on obstructed distances. A comprehensive experimental evaluation using both real and synthetic datasets has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms.

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Section: Queries With Obstaclesmentioning

confidence: 99%

Section: Definition 6 (Continuous Obstructed Nearest Neighbor Query)mentioning

confidence: 99%

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Continuous obstructed nearest neighbor queries in spatial databases

Gao

Zheng

2009

Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data

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“…Obstacles Manypublications deal with various data mining techniques in the presence of obstacles, e.g., [THH01,ZPMZ04]. However, in these methods obstacles are considered impenetrable objects that need to be bypassed, for example, to compute the distance between twoobjects as done in [ZPMZ04].…”

Section: R Elated Work Anomaly Detection Wu Et Al [Wcdmentioning

confidence: 99%

“…However, in these methods obstacles are considered impenetrable objects that need to be bypassed, for example, to compute the distance between twoobjects as done in [ZPMZ04]. In contrast, we consider obstacles to be permeable albeit having different properties than their surroundings.…”

Section: R Elated Work Anomaly Detection Wu Et Al [Wcdmentioning

confidence: 99%

In-network detection of anomaly regions in sensor networks with obstacles

Franke

Karnstedt

Klan

et al. 2009

Comp. Sci. Res. Dev.

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In the past couple of years, sensor networks have evolved to ap owerful infrastructure component for monitoring and tracking events and phenomena in many application domains. An important task in processing streams of sensor data is the detection of anomalies, e.g., outliers or bursts, and in particular the computation of the location and spatial extent of such anomalies in as ensor network. In this paper, we present an approach that facilitates the efficient computation of such anomaly regions from sensor readings. We propose an algorithm to derive spatial regions from individual anomalous sensor readings, with aparticular focus on obstacles present in the sensor network. We improve this approach by proposing adistributed in-network processing technique where the region detection is performed at the sensor nodes. We demonstrate the advantages of this strategy overac entralized processing strategy by utilizing acost model for real sensors and sensor networks. 1I ntroductionDrivenb ym ajor advancements in sensor technology,s everal sensor networks have been and are being deployed in various application domains such as the monitoring of traffic, buildings, rivers, and the environment in general. Typical examples for environmental monitoring include precision agriculture (e.g., observing the humidity of the soil) and monitoring particles in urban areas to react to changes in air quality measures. An important objective in processing sensor data is the detection of anomalies that occur,e.g., in the form of outliers or bursts. This kind of data processing and analysis not only reduces the volume of data reaching end user applications butitalso simplifies the further processing and interpretation of the sensor data.By analyzing individual and aggregated sensor measurements, one can obtain useful information about the locations where anomalous events and phenomena occur.Such location information then can be visualized on amap and interpreted for individual sensors. In particular it can be used to derive anomaly regions.Such regions are composed of neighboring *

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Nearest surrounder searching in mobile computing environments

Chung

Jang

Jung

et al. 2012

Int J Communication

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The key issue in mobile computing environments (MCEs) is how to relieve communication congestion and provide accurate information through location-based services (LBSs). The nearest surrounder (NS) query, used to find all visible objects around a given location, is a type of spatial query that suggests broad application base in LBS domain. However, because existing works for NS query only take into account static query points, the application of the NS query is limited to various LBSs in MCEs requiring frequent location updates. Motivated by this limitation, this paper introduces the continuous nearest surrounder (CNS) query, which uses a decentralized system framework to continuously maintain updated query results in MCEs. In this framework, the LBS server executes an initial NS query to prepare a region, termed non-provoked polygon (NPP), defines a set of visible objects that cannot be changed. Conversely, a client caches the NPP and does not update request unless it leaves its NPP. We performed extensive experiments using synthetic and real datasets with various data cardinality, and query mobility to validate the accurate performance of the proposed strategy. The results show that the CNS algorithm outperforms NS, in terms of computation and communication costs as well as scalability.ADV. PROCESSING TECHNOLOGIES AND APPLICATIONS FOR MOBILE COMM. SYSTEMS 771 the location of the mobile device which moves continuously and randomly, NS results become obsolete whenever a user moves to a new location. Figure 1 summarizes the concept of the NS query for a user moving in an MCE. Let Q be a query route Q D ¹Q 1 , Q 2 , : : : , Q m º in a time interval OEt s , t e , where Q i D .q i ,1 , q i ,2 / in R 2 . Initially, the result of the NS query at Q 1 is NS

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Spatial Queries in the Presence of Obstacles

Cited by 47 publications

References 20 publications

Continuous obstructed nearest neighbor queries in spatial databases

Continuous obstructed nearest neighbor queries in spatial databases

In-network detection of anomaly regions in sensor networks with obstacles

Nearest surrounder searching in mobile computing environments

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