Continuous Monitoring of Distance-Based Range Queries

Cheema, Muhammad Aamir; Branković, Ljiljana; Lin, Xuemin; Zhang, Wenjie; Wang, Wei

doi:10.1109/tkde.2010.246

Cited by 50 publications

(36 citation statements)

References 36 publications

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“…It is a world of 5000 × 5000km 2 , comprising of 175,813 nodes and 179,179 edges. It has been used in many performance evaluations as well [8,11]. The direction to the edges is assigned randomly and skewed data points distribution of is opted.…”

Section: Methodsmentioning

confidence: 99%

A Pruning Algorithm for Reverse Nearest Neighbors in Directed Road Networks

Qamar¹,

Attique²,

Chung³

2015

IJNDC

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In this paper, we studied the problem of continuous reverse k nearest neighbors (RkNN) in directed road network, where a road segment can have a particular orientation. A RNN query returns a set of data objects that take query point as their nearest neighbor. Although, much research has been done for RNN in Euclidean and undirected network space, very less attention has been paid to directed road network, where network distances are not symmetric. In this paper, we provided pruning rules which are used to minimize the network expansion while searching for the result of a RNN query. Based on these pruning rules we provide an algorithm named SWIFT for answering RNN queries in continuous directed road network.

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Section: Methodsmentioning

confidence: 99%

A Pruning Algorithm for Reverse Nearest Neighbors in Directed Road Networks

Qamar¹,

Attique²,

Chung³

2015

IJNDC

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“…Focusing on processing moving monitoring queries over static objects, the safe region methods were also proposed in [8,9]. Similarly to the safe region assigned to a moving object, the safe region assigned to a query q is the region that (i) contains q.p and (ii) guarantees that while q.p remains inside it, the result of q remain unchanged.…”

Section: Related Workmentioning

confidence: 99%

“…In the former case, the server assigns o a new vicinity region together with new query segments, while in the latter case, the server updates the result of q accordingly. (20) o 3 (8) o 4 (18) o 5 (25) o 6 (6) q.p (b) 10 distance between o 3 and q.p One critical problem is how to determine the suitable size of a vicinity region VR(o) for each moving object o. If VR(o) is too small, o needs to frequently send a location-update to the server for receiving a new vicinity region.…”

Section: Introductionmentioning

confidence: 99%

The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks

Jung

Kim

2017

IJGI

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This paper addresses the problem of processing range monitoring queries, each of which continuously retrieves moving objects that are currently located within a given query range. In particular, this paper focuses on processing range monitoring queries in the road network, where movements of the objects are constrained by a predefined set of paths. One of the most important challenges of processing range monitoring queries is how to minimize the wireless communication cost and the server computation cost, both of which are heavily dependent on the amount of location-update stream generated by moving objects. The traditional centralized methods for range monitoring queries assume that moving objects periodically send location-updates to the server. However, when the number of moving objects becomes increasingly large, such an assumption may no longer be acceptable because the amount of location-update stream becomes enormous. Recently, some distributed methods have been proposed, where moving objects utilize their available computational capabilities for sending location-updates to the server only when necessary. Unfortunately, the existing distributed methods only deal with the objects moving in Euclidean space, and thus they cannot be extended to processing range monitoring queries over the objects moving along the road network. In this paper, we propose the distributed method for processing range monitoring queries in the road network. To utilize the computational capabilities of moving objects, we introduce the concept of vicinity region. A vicinity region, assigned to each moving object o, makes o monitor whether or not it should be included in the results of nearby queries. The proposed method includes (i) a new spatial index structure, called the Segment-based Space Partitioning tree (SSP-tree) whose role is to efficiently search the appropriate vicinity regions for moving objects based on their heterogeneous computational capabilities and (ii) the details of the communication strategy between the server and moving objects, which significantly reduce the wireless communication cost as well as the server computation cost. Through simulations, we verify the effectiveness for processing range monitoring queries over a large number of moving objects (up to 100,000) in the road network (modeled as an undirected graph).

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“…10 shows A i .max = dist(q, a) and A i .min = dist(q, b). We remark that A i .max and A i .min can be computed following the ideas presented in [23,24]. Note that an entry e cannot be pruned by A i if mindist(q, e) > A i .max or maxdist(q, e) < A i .min.…”

Section: Algorithm 1 Pruneentry(e A)mentioning

confidence: 99%

Relaxed Reverse Nearest Neighbors Queries

Hidayat

Cheema

Taniar

2015

Advances in Spatial and Temporal Databases

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Abstract. Given a set of users U , a set of facilities F , and a query facility q, a reverse nearest neighbors (RNN) query retrieves every user u for which q is its closest facility. Since q is the closest facility of u, the user u is said to be influenced by q. In this paper, we propose a relaxed definition of influence where a user u is said to be influenced by not only its closest facility but also every other facility that is almost as close to u as its closest facility is. Based on this definition of influence, we propose relaxed reverse nearest neighbors (RRNN) queries. Formally, given a value of x > 1, an RRNN query q returns every user u for which dist(u, q) ≤ x × N N Dist (u) where N N Dist(u) denotes the distance between a user u and its nearest facility. Based on effective pruning techniques and several non-trivial observations, we propose an efficient RRNN query processing algorithm. Our extensive experimental study conducted on several real and synthetic data sets demonstrates that our algorithm is several orders of magnitude better than a naïve algorithm as well as a significantly improved version of the naïve algorithm.

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Continuous Monitoring of Distance-Based Range Queries

Cited by 50 publications

References 36 publications

A Pruning Algorithm for Reverse Nearest Neighbors in Directed Road Networks

A Pruning Algorithm for Reverse Nearest Neighbors in Directed Road Networks

The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks

Relaxed Reverse Nearest Neighbors Queries

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