scholarly journals A Safe Exit Algorithm for Continuous Nearest Neighbor Monitoring in Road Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 37-53 ◽  
Author(s):  
Hyung-Ju Cho ◽  
Se Jin Kwon ◽  
Tae-Sun Chung
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Road Networks ◽  
Experimental Results ◽  
Exit Point ◽  
Communication Costs ◽  
Query Result ◽  
Safe Region ◽  
Conventional Algorithm

Query processing in road networks has been studied extensively in recent years. However, the processing of moving queries in road networks has received little attention. In this paper, we introduce a new algorithm called the Safe Exit Algorithm (SEA), which can efficiently compute the safe exit points of a moving nearest neighbor (NN) query on road networks. The safe region of a query is an area where the query result remains unchanged, provided that the query remains inside the safe region At each safe exit point, the safe region of a query and its non-safe region meet so that a set of safe exit points represents the border of the safe region. Before reaching a safe exit point, the client (query object) does not have to request the server to re-evaluate the query This significantly reduces the server processing costs and the communication costs between the server and moving clients. Extensive experimental results show that SEA outperforms a conventional algorithm by up to two orders of magnitude in terms of communication costs and computation costs.

scholarly journals A Distributed Approach to Continuous Monitoring of Constrained k-Nearest Neighbor Queries in Road Networks

2012 ◽  
Vol 8 (2) ◽  
pp. 107-126 ◽  
Author(s):  
Hyung-Ju Cho ◽  
Seung-Kwon Choe ◽  
Tae-Sun Chung
Keyword(s):  
Query Processing ◽  
Continuous Monitoring ◽  
Nearest Neighbor ◽  
Road Networks ◽  
K Nearest Neighbor ◽  
Central Processing ◽  
Distributed Approach ◽  
Query Result ◽  
On The Road ◽  
Road Segments

Given two positive parameters k and r, a constrained k-nearest neighbor (CkNN) query returns the k closest objects within a network distance r of the query location in road networks. In terms of the scalability of monitoring these CkNN queries, existing solutions based on central processing at a server suffer from a sudden and sharp rise in server load as well as messaging cost as the number of queries increases. In this paper, we propose a distributed and scalable scheme called DAEMON for the continuous monitoring of CkNN queries in road networks. Our query processing is distributed among clients (query objects) and server. Specifically, the server evaluates CkNN queries issued at intersections of road segments, retrieves the objects on the road segments between neighboring intersections, and sends responses to the query objects. Finally, each client makes its own query result using this server response. As a result, our distributed scheme achieves close-to-optimal communication costs and scales well to large numbers of monitoring queries. Exhaustive experimental results demonstrate that our scheme substantially outperforms its competitor in terms of query processing time and messaging cost.

scholarly journals k-Nearest Neighbor Search based on Node Density in MANETs

2014 ◽  
Vol 10 (4) ◽  
pp. 385-405 ◽  
Author(s):  
Yuka Komai ◽  
Yuya Sasaki ◽  
Takahiro Hara ◽  
Shojiro Nishio
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
K Nearest Neighbor ◽  
Node Density ◽  
Neighbor Search ◽  
Query Result ◽  
Knn Query ◽  
The One ◽  
K Nearest Neighbor Search

In a kNN query processing method, it is important to appropriately estimate the range that includes kNNs. While the range could be estimated based on the node density in the entire network, it is not always appropriate because the density of nodes in the network is not uniform. In this paper, we propose two kNN query processing methods in MANETs where the density of nodes is ununiform; the One-Hop (OH) method and the Query Log (QL) method. In the OH method, the nearest node from the point specified by the query acquires its neighbors' location and then determines the size of a circle region (the estimated kNN circle) which includes kNNs with high probability. In the QL method, a node which relays a reply of a kNN query stores the information on the query result for future queries.

An Efficient Optimization Approach for Top-k Queries on Uncertain Data

2018 ◽  
Vol 27 (01) ◽  
pp. 1741002 ◽  
Author(s):  
Zhiqiang Zhang ◽  
Xiaoyan Wei ◽  
Xiaoqin Xie ◽  
Haiwei Pan ◽  
Yu Miao
Keyword(s):  
Query Processing ◽  
Uncertain Data ◽  
Experimental Results ◽  
Optimization Approach ◽  
Relationship Analysis ◽  
Query Result ◽  
Query Type ◽  
Text Query

Uncertain data is inherent in various important applications and Top-[Formula: see text] query on uncertain data is an important query type for many applications. To tackle the performance issue of evaluating Top-[Formula: see text] query on uncertain data, an efficient optimization approach was proposed in this paper. This method can anticipate the tuples most likely to become Top-[Formula: see text] result based on dominant relationship analysis, greatly reducing the amount of data in query processing. When the database is updated, this method could determine whether the change affects the current query result, and help us to avoid unnecessary re-query. The experimental results prove the feasibility and effectiveness of this method.

On Efficient Aggregate Nearest Neighbor Query Processing in Road Networks

2015 ◽  
Vol 30 (4) ◽  
pp. 781-798 ◽  
Author(s):  
Wei-Wei Sun ◽  
Chu-Nan Chen ◽  
Liang Zhu ◽  
Yun-Jun Gao ◽  
Yi-Nan Jing ◽  
...  
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Query ◽  
Aggregate Nearest Neighbor

Continuous K Nearest Neighbor Queries of Moving Objects in Road Networks

2010 ◽  
Vol 33 (8) ◽  
pp. 1396-1404 ◽  
Author(s):  
Liang ZHAO ◽  
Luo CHEN ◽  
Ning JING ◽  
Wei LIAO
Keyword(s):  
Moving Objects ◽  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Queries

NNlists-based k-path nearest neighbor query in road networks

2010 ◽  
Vol 30 (7) ◽  
pp. 1947-1949
Author(s):  
Bao-wen WANG ◽  
Jing-jing HAN ◽  
Zi-jun CHEN ◽  
Wen-yuan LIU
Keyword(s):  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Query

Aggregate keyword nearest neighbor queries on road networks

2017 ◽  
Vol 22 (2) ◽  
pp. 237-268 ◽  
Author(s):  
Pengfei Zhang ◽  
Huaizhong Lin ◽  
Yunjun Gao ◽  
Dongming Lu
Keyword(s):  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Queries
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